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                            [body] => 7 Skills Needed to Become a Machine Learning EngineerLast Updated : 22 Dec, 2020Do you want to transition to becoming a Machine Learning Engineer? If so, then you are not alone! Technologies like Artificial Intelligence, Machine Learning, Data Science, etc. are becoming more and more popular these days. But these technologies are also thrown about like buzz words where many people don’t know what they really mean or the skills required for learning them. That’s where this article can help you by listing all the different skills you need to bag your dream job as a Machine Learning Engineer!Machine Learning is a technology that is focused on machines independently learning from the data without much human intervention or explicit programming. This is a complex field that is a distillation of Artificial Intelligence and interlinked with other technologies like Data Science. But this creates the question of Who is a Machine Learning Engineer. And how is he different from a Data Scientist or a Data Analyst? Let’s understand this first.Who is a Machine Learning Engineer?Since Machine Learning is deeply linked with Data Science, there are some overlaps in the job description of a Data Scientist, Data Analyst, or a Machine Learning Engineer. However, the main differences are what they focus on. Data Scientists and Analysts are mainly focused on obtaining insights from the data and presenting them to the higher-ups in the organization to make important decisions. This involves having some knowledge of ML algorithms. However, Machine Learning Engineers are excluding focused on Machine Learning. Their aim is to create software components that can work with minimal human supervision and help in obtaining insights from the data they are provided with. This is why there is so much focus on Computer fundamentals and software development for a Machine Learning Engineer because that is their specialization.  Now let’s see the skills you need to become a Machine Learning Engineer. While the first two of these are quite basic that you may have even learned in your high school or bachelor’s, they become more complex and domain-specific as you move through the list.Skills Needed for Becoming a Machine Learning Engineer. 1. Applied MathematicsMaths is quite an important skill in the arsenal of a Machine Learning engineer. It is also one of the basic subjects that are taught right from school and that’s why it is the first skill on our list. But are you wondering why you need maths at all? (Especially if you don’t like it?!!) Well, maths can have many uses in ML. You can apply various mathematical formulas in selecting the correct ML algorithm for your data, you can use maths to set parameters, approximate confidence levels, Many of the ML algorithms are applications derived from statistical modeling procedures and so it’s very easy to understand them if you have a strong foundation in Maths. Some of the important topics of maths that you need to know include linear algebra, probability, statistics, multivariate calculus, distributions like Poisson, normal, binomial, etc. Apart from Maths, having some knowledge of Physics concepts can also be beneficial if you want to become a Machine Learning engineer.2. Computer Science Fundamentals and ProgrammingThis is another basic requirement for becoming a good machine learning engineer. You need to be familiar with different CS concepts like data structures (stack, queue, tree, graph), algorithms (searching, sorting, dynamic and greedy programming), space and time complexity, etc. The good thing is you probably know all of this if you have done your bachelor’s in computer science! You should be well versed in different programming languages like Python and R for ML and statistics, Spark and Hadoop for distributed computing, SQL for database management, Apache Kafka for data pre-processing, etc. Python is a very popular programming language especially for Machine Learning and Data Science so it’s great if you are well versed in its libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.3. Machine Learning Algorithms. What is a very important skill in becoming a Machine Learning Engineer? Obviously, it’s very important to know all the common machine learning algorithms so that you know where to apply what algorithms. Mostly ML algorithms are divided into 3 common types namely, Supervised, Unsupervised, and Reinforcement Machine Learning Algorithms. In detail, some of the common ones include Naïve Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc. So it’s good if you have a sound knowledge of all these algorithms before beginning your journey as an ML engineer.4. Data Modeling and EvaluationAs a machine learning engineer, you should be skilled in data modeling and evaluation. After all, data is your bread and butter! Data modeling involves understanding the underlying structure of the data and then finding patterns that are not obvious to the naked eye. You also need to evaluate the data using an algorithm that is suitable for the data. For example, the type of machine learning algorithms to use such as regression, classification, clustering, dimension reduction, etc. depends on the data. A classification algorithm well suited to large data and speed may be naive beyes, or a regression algorithm for accuracy might be a random forest. Similarly, a clustering algorithm for categorical variables is k mode while for probability is k means. You need to know all these details about various algorithms to contribute to data modeling and evaluation effectively.5. Neural NetworksNobody can forget the importance of Neural Networks in the life of an ML engineer! These Neural Networks are modeled after the neurons in the human brain. They have multiple layers that include an input layer that receives data from the outside world which then passes through multiple hidden layers that transform the input into data that is valuable for the output layer. These demonstrate a deep insight into parallel and sequential computations that are used to analyze or learn from the data. There are many different types of neural networks like Feedforward Neural Network, Recurrent Neural Network, Convolutional Neural Network, Modular Neural Network, Radial basis function Neural Network, etc. While it’s not necessary that you understand all these neural networks in detail to become an ML engineer, it’s important that you know the core fundamentals. And you can always learn the rest on the way!6. Natural Language ProcessingNatural Language Processing is naturally quite important and a fundamental part of Machine Learning. In essence, NLP aims to teach the human language with all its complexities to computers. This is so that machines can understand and interpret the human language to eventually understand human communication in a better way. There are many different libraries that provide the foundation of Natural Language Processing. These libraries have various functions that can be used to make computers understand natural language by breaking the text according to its syntax, extracting the important phrases, removing extraneous words, etc. You can be familiar with some or even one of these libraries like the Natural Language Toolkit which is the most popular platform for creating applications relating to NLP.7. Communication SkillsAnd finally, we come to a skill that is a soft skill and may not be considered that important. However, if you are good at communication skills, it can make a world of difference in your career trajectory. That’s because while you understand the data and the insights obtained using machine learning better than anyone else, it is equally important that you can convey these insights to a non-technical team, your shareholders, or clients. This can also involve data storytelling where you should be able to present your data in a storytelling format with a beginning and ending at concrete results that you have obtained from the data using machine learning. That’s because eventually, the data analysis is less important to a company than the actionable insights that can be obtained from the data. And if you are able to convey these insights, then the sky’s your limit in your profession!Conclusion. Machine Learning is becoming more and more widespread and is used in almost every field these days. Whether it be medicine, cybersecurity, automobiles, etc. all these fields are exploring the capabilities of machine learning. It’s obvious that learning more about ML and becoming a Machine Learning Engineer is a great idea and may even be a very wise career move! So check out all these skills and start learning them so you can improve your capabilities and bag your dream job as a Machine Learning Engineer!My Personal Notes arrow_drop_upSave LikePreviousClass 8 RD Sharma Solutions - Chapter 21 Mensuration II (Volumes and Surface Areas of a Cuboid and a Cube)- Exercise 21.4 | Set 1Next How to Create a New Project in Android Studio Canary Version with Jetpack Compose?Recommended ArticlesPage :7 Skills Needed to Become a Business Intelligence Analyst16, Dec 20Skills Required to Become a Cloud Engineer03, Dec 20Want to Become an Android Developer - Learn these 5 Tech Skills!10, Feb 21Skills Required to Become a Ethical Hacker04, Jun 20Support vector machine in Machine Learning20, Dec 20Azure Virtual Machine for Machine Learning06, Dec 19Artificial intelligence vs Machine Learning vs Deep Learning23, Jan 19How to Start Learning Machine Learning?11, Sep 19Difference Between Artificial Intelligence vs Machine Learning vs Deep Learning01, May 20Difference Between Machine Learning and Deep Learning26, May 20Need of Data Structures and Algorithms for Deep Learning and Machine Learning14, Oct 20Learning Model Building in Scikit-learn : A Python Machine Learning Library17, Feb 17Top 10 Algorithms every Machine Learning Engineer should know22, May 19Blog | Software Engineer Skills13, May 19Machine Learning - Applications11, Jan 16Demystifying Machine Learning02, May 16An introduction to Machine Learning24, Aug 17Cross Validation in Machine Learning21, Nov 17How To Use Classification Machine Learning Algorithms in Weka ?04, May 21Regression and Classification | Supervised Machine Learning01, Dec 17Clustering in Machine Learning15, Jan 18Difference between Machine learning and Artificial Intelligence19, Feb 18Top 10 Apps Using Machine Learning in 202010, Apr 20Machine Learning and Artificial Intelligence23, Apr 18Article Contributed By :[email protected] for difficultyEasy Normal Medium Hard ExpertArticle Tags :GBlogMachine LearningPractice Tags :Machine LearningReport IssueWriting code in comment? Please use ide.geeksforgeeks.org, generate link and share the link here. Load CommentsWhat's NewComplete Interview PreparationView DetailsSystem Design-Live Classes for Working ProfessionalsView DetailsData Structures & Algorithms- Self Paced CourseView DetailsMost popular in GBlogTop 10 Programming Languages to Learn in 20225 Ways To Increase Your Chances of Getting a JobTop 7 Game Development Trends For 2022Top Programming Languages for Android App DevelopmentAdvantages and Disadvantages of InternetMost visited in Machine LearningNaive Bayes ClassifiersRemoving stop words with NLTK in PythonLinear Regression (Python Implementation)Web MiningReinforcement learning× We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy Got It ! Start Your Coding Journey Now!Login Register
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                            [body] => What Skills Do You Need to Become a Machine Learning Engineer?As artificial intelligence plays a growing role in our lives, the demand for machine learning engineers has skyrocketed. Read on to learn more about the skills needed to become an ML engineer.Here’s what we’ll cover: Essential technical skills for ML engineers Essential soft skills for ML engineers Essential tools/programs to master for ML engineers Essential certifications for ML engineers Machine learning engineering is a cornerstone of AI—without it, recommendation algorithms like those used by Netflix, YouTube, and Amazon; technologies that involve image or voice recognition; and many of the automated systems that power the products and services we use wouldn’t work. That’s because machine learning engineering sits at the intersection of data science and software engineering; where a data scientist will analyze collected data to tease out actionable insights, a machine learning engineer will design the self-running software that makes use of that data and automates predictive models. In action, if a machine learning engineer were to work on a recommendation algorithm for YouTube, they might be tasked with developing a machine learning pipeline that integrates into the platform so that a user can click on the “next” button once a video finishes playing and watch another video that is relevant to their interests, as determined by the data-informed algorithm and predictive models developed by YouTube’s data specialists. The interdisciplinary nature of the role means that machine learning engineers are well versed in foundational data science skills such as understanding data structures and data modeling, quantitative analysis methods, building out data pipelines, and statistics, while also having computer science fundamentals and software engineering skills such as understanding computer architecture, proficiency with programming languages, and the ability to program software solutions. Learn more about how to become a machine learning engineer in this guide. Essential technical skills for ML engineers. Machine learning engineering combines software engineering principles with analytical and data science knowledge in order to make a machine learning model usable to a piece of software or person. This means that machine learning engineers need to have a slate of skills that span both data science and software engineering. Software engineering skills. Some of the computer science fundamentals that machine learning engineerings rely on include: writing algorithms that can search, sort, and optimize; familiarity with approximate algorithms; understanding data structures such as stacks, queues, graphs, trees, and multi-dimensional arrays; understanding computability and complexity; and knowledge of computer architecture such as memory, clusters, bandwidth, deadlocks, and cache. Data science skills. Some of the data science fundamentals that machine learning engineers rely on include familiarity with programming languages such as Python, SQL, and Java; hypothesis testing; data modeling; proficiency in mathematics, probability, and statistics (such as the Naive Bayes classifiers, conditional probability, likelihood, Bayes rule, and Bayes nets, Hidden Markov Models, etc.), and being able to develop an evaluation strategy for predictive models and algorithms. Additional machine learning skills. Many machine learning engineers are also trained in deep learning, dynamic programming, neural network architectures, natural language processing, audio and video processing, reinforcement learning, advanced signal processing techniques, and the optimization of machine learning algorithms. Essential soft skills for ML engineers. Soft skills are what set apart effective engineers from those who flounder. While machine learning engineering is, at its core, a technical job, soft skills such as the ability to clearly communicate, problem solve, manage time, and collaborate with others are what lead to a project’s successful completion and delivery. Communication skills. It is not unusual for machine learning engineers to work with data scientists and analysts, software engineers, research scientists, marketing teams, and product teams, so the ability to clearly communicate to stakeholders the project goals, timelines, and expectations is a crucial part of the job. Problem-solving skills. The ability to problem solve is important for both data scientists and software engineers and essential for machine learning engineers. Machine learning focuses on solving real-time challenges, so the ability to think critically and creatively about issues that arise and develop solutions is a foundational skill. Domain knowledge. In order to design self-running software and optimize solutions used by businesses and customers, machine learning engineers need to understand both the needs of the business and the kinds of problems that their designs are solving. Without domain knowledge, a machine learning engineer’s recommendations may lack precision, their work may overlook features that are useful, and it may be difficult to evaluate a model. Time management. Machine learning engineers often juggle demands from different stakeholders while having to find the time to perform research, organize and plan projects, design software, and test it rigorously. Being able to manage one’s time is key to making valuable contributions to the team.  Teamwork. Machine learning engineers are often at the center of AI initiatives within an organization, so they inevitably work with data scientists, software engineers, marketers, product designers and managers, and testers. The ability to collaborate with others and contribute to a supportive work environment is a skill that many managers look for when hiring a machine learning engineer. Thirst for learning. The fields of artificial intelligence, deep learning, machine learning, and data science are rapidly evolving, and even those who have earned graduate degrees and work as machine learning engineers find ways to continue their education through bootcamps, workshops, and self-study. Whether it’s learning the latest programming languages, mastering new tools or programs, or studying the latest breakthrough techniques and technologies, the most effective machine learning engineers are constantly refreshing their toolkit and open to learning new skills.  Essential tools/programs to master for ML engineers. In addition to a solid understanding of programming and scripting languages such as Python, SQL, Java, and C++, many machine learning engineers are also proficient in the following programs and tools: TensorFlow Spark and Hadoop R Programming Apache Kafka Weka MATLAB Google Cloud ML Engine Amazon Machine Learning PytorchJupyter Notebook IBM Watson Essential certifications for ML engineers. Most machine learning engineering roles require a candidate to hold at least a bachelor’s degree in a related field such as computer science, mathematics, or statistics, and some require a master’s degree or Ph.D. in machine learning, computer vision, neural networks, deep learning, or a related field. Beyond higher education, certifications in machine learning, artificial intelligence, or data science are useful because of the relevant skills they impart. There is no shortage of universities and online training programs offering machine learning certifications, each taking a different instructional approach and focusing on different facets of machine learning, so it’s important to examine closely the skills taught, the hands-on experience offered, and whether there are opportunities to work on projects that involve the full machine learning pipeline. It’s also useful to cross-check program curricula against the requirements of recent machine learning engineering job listings. For example, a machine learning engineering role at Facebook requires a degree in a related field, plus experience with deep learning, reinforcement learning, privacy-preserving machine learning, large scale personalization, artificial intelligence, and experience with developing machine learning algorithms and infrastructure in C/C++ or Python. Meanwhile, an Apple job listing requires a degree in a related field, plus experience with applying machine learning to solve real business problems, experience with cloud platforms such as AWS, experience with machine learning frameworks such as Scikit-Learn and TensorFlow, and experience with data processing. The expectations placed on a machine learning engineer will differ depending on the organization and team they join. Whatever certification a candidate pursues, it’s important that it equips them with foundational skills that are transferable and can serve as the building blocks of their career.  Is machine learning engineering the right career for you? Knowing machine learning and deep learning concepts is important—but not enough to get you hired. According to hiring managers, most job seekers lack the engineering skills to perform the job. This is why more than 50% of Springboard's Machine Learning Career Track curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service. No other bootcamp does this. Our machine learning training will teach you linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them. Find out if you're eligible for Springboard's Machine Learning Career Track.Other articles you may be interested inPyTorch vs. TensorFlow: How Do They Compare?What Is Tiny Machine Learning?Springboard vs. Other Online Bootcamps: How Do They Compare?Related ArticlesPyTorch vs. TensorFlow: How Do They Compare?What Is Tiny Machine Learning?Springboard vs. Other Online Bootcamps: How Do They Compare?Ready to learn more?Browse our Career Tracks and find the perfect fitLearn moreCAREER TRACKSData Science BootcampSoftware Engineering BootcampUI/UX Design BootcampUX BootcampMachine Learning BootcampData Analytics BootcampData Engineering BootcampCyber Security BootcampRESOURCESFree Learning PathsE-books and GuidesBlogCareer Assessment TestABOUT USAbout the CompanyJobsContact UsBecome a MentorHire Our StudentsAffiliatesPartnersCommunityUniversitiesGET SOCIALSCHOLARSHIPSStudentsVeteransWomen in TechCopyright 2021TermsPrivacyConduct
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                                            [title] => 10 Must Have Machine Learning Engineer Skills That Will Get ...
                                            [url] => https://www.youtube.com/watch?v=Dml0gMJxwWI
                                            [thumbnail] => data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAkGBwgHBgkIBwgKCgkLDRYPDQwMDRsUFRAWIB0iIiAdHx8kKDQsJCYxJx8fLT0tMTU3Ojo6Iys/RD84QzQ5OjcBCgoKDQwNGg8PGjclHyU3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3N//AABEIAEYAfAMBIgACEQEDEQH/xAAcAAAABwEBAAAAAAAAAAAAAAAAAQIDBAUGBwj/xAA5EAACAQMDAgQDBwIEBwAAAAABAgMABBEFEiEGMRMiQVEUMmEHI1JxgZGhFULB0eHwFjVjc4KU0v/EABoBAAIDAQEAAAAAAAAAAAAAAAIDAQQFAAb/xAArEQACAgEDBAADCQAAAAAAAAAAAQIDERIhMQQTQVEUkeEyM0JSYXGBobH/2gAMAwEAAhEDEQA/AMNpljLqd9HZ27RrLJnaZGIXgE+gJ9PQGrpOjtQkEPhXFq7OqMwy4C72Krzt5HGcjj2zwTmsA8EZHtQ2IRyq/tW81LOzFs0kPSN9NapNHcWhLiNkGXAKP2Py5yPUY4988VU6jZvp97JaSsGeILuK9slQxH6E4z64zUPYv4R+1LUADAGBRxUk92KkKFKpIo6ahLFClCk0YoxbHBShSBShXCpIeQFiAoJJ7AVKjtZD820Y9NwJ/SmbRsFwM7ivGP5qZHayyBWXBBPPPy/nU5KtjecIb8BsZQ78DJAHIpKmpWyRfJLxInyN6H6GmJ8bwy8Bhntjn1/z/WpTEZecMMGl5pkGlZogHEH2e2EGp9W2dncwwyxyrL5J4xImRGxBKnvggH9K6MdC0u2tNUup9F0OYWU/w6RjTlXe52hWJzwMt2rl3SWtDp7qC11Vrc3Ag3/dh9u7cpXvg+9bNvtM0+S2v7eXQZ3jvpTJKDeDhiB2wnGMA1hdVXdKea/9PY0WVxzrWePr/Rf6poemaQt5BPoeh3EyWXxMMi2CoAQwUqRzkc8GpejdP6FfXd3bR6Tod3GkIaO8i09VVXORtIzz6Hg1jZPtBsriG6S702/uJLiHwDNJepuRM5wuI8d/XFXY+2WIDA0F/wD2h/8ANV1R1WfPz+odl1GjCW/vH7FhFo2kf0jc2g6KbttSNksnwShFG/bu2/oeM0qbQdEfpbqB30TTFvLBbiJZ4bVUyVTKsB6HkftWZP2i2DWUlqNGugHujdLILxd0chbdkeTHB96KT7R7b+h6jpsWkz775JRJcS3QZi7rgsQEA9uBjtRQo6pSWc/MC23p3BqK3z6OfijFJFKFbxktD6xeRW8x3ccDOKJhtYj2poDB7kenBpa8DAom1giWlrZFrp1qN0UwkBYsMY7DmrcOAu1RnIGcVnba6MBJ5xt7e5yaurS6gmCCU+GTyKCXsmFcJLdEuJEc4AyO+3PaoWqWQhQSiRVXsIz/AIVJne/kglTp+2inliXc+9gMD3AJGf3/AHrPQ6hNfw77hCkm7DA57/r2pEL1K11+UTf08IVams/qOg0sGmQaPNXDLcSu0swjUbc3KRPCH8yyttU8epwcfqMe/Ga0DDQ/ugHszP8AEqZDsATwfut4wCRv+bkeU4k24ytVXS1nFf6/Z2k6ho5XKkEfQ10aXoWwQPtghYi5WGMbT5w3Zu/A5H8+1Z01HUsywb7lh4wYOe2sZHna3lsgr2UYiBmVcSho92cng4D96a1FraLTrN7b4M3CY3IirJuIQZYn2Ld1cHk+Ula3dx0do9q0huFVYhIqRyLEx3bl3BiN2QMfmfpSouhrGRwGijRD4O1tuc+Iue27jHb61ycFhuWxOMmSnl0uW4uyq2KwCWdTtQKxj8MeD4ePXfuyRz8ueKe0s6KbCx+JFqJljyxYplnzKMEYyOChy2V4Fae36GsJvAbbEElWE7ghO1nAJU89wGB+uaiL0zpbSXKrCuIIfFywHm5HHlYj19zXJQksKRPZbM9EdO8SAzDThCWPjg4L7/E8uNn9uzbnHlxv/uxTqjRDZJ8X8KLyMpLKLf5ZGXOYxjgBlIBxxuFaZOkdNeOyKQK0l0V2rtGFyxH4s+me2KlL0Tpn3/3S/dsQp8M4YbN4Y+bIyPoaFzgvxMj4eXtGJ11dOle3i097SKJ52VpUKeUGRwCRjdgLtPJxjFQ9d/p733jaUcWsqArHtwYyPKQR9du7/wAq6MvQ+mbl+5i2EJhgp7spYjv3GB+4padE6Y20/DwlSqHdtPG4biO/cDH55FdHqa443YL6RvyjlKnPbvV7c9M6hZaJaa4uye0mTLGIkmHP4/8APtn+bfrLRdP0zS0ls7cRl2QhipU4Ofqfatx9l8yXXRtvEwDrG0kbBhnI3Hgj2waK3qnGCsjxkX8Ppnpb8ZOadBXk79SXEaOvw8dvm6kYZ2gsAoHI5Gcn6A1npOoLXW+prgRRNDDcznwWbGSPTcPQnGfzOK6z1B0Np2i6L1Df6AjWz3FnK8luGyhIilHk9V+fOO3AAArzzph2ahaODyJ049+RVaq1SulcueBtkM0Ktv2/5OgvZ7SVCuMepqGeCRmrafsfb8v9cVTNnc351rp5RhQzLkjaNezafqdvdW0fiTo33aY7seBj681rpur+prZh8Rpd1CxKqBLC6nJLbQMr3JL49+cduMbptwtpqVndSBikFxHKwXuQrAnH14rXW3V2l2d5dvFaSy215ewzzRC1SHyIZCeztlwzRsDxkrzj1oWNriOTe0p7ik6w6kESSrp1wY94gSQxOw8QEgKGK/OCSPxelNRdW68k/wB1p8oldYnwImJZQQkZxjtnCg+p470Z6s0qS/hv5Le9E0bQKI0GFCR3Pi5yJAGypPlKHDchqkW/XFgvw6zWdywto7VYpFVdw2TpJKp83KkRrt9jntnNDrn+QnGOBMfUHVVuz7NHv4yixs4NrJ5QgwhIK8AAHn1xTM3VWtafOJLrTTaPMm0Ca28MOvHZSuCO3pUa81vRp9N1GzWJx8VOJkb+mRgIdjr8vjcN5h58n14pHVPUdtriMtvbPBi9knXygB0ZEGX8x8+U9OMY9aOMpN4cSHOS8kode6koUeDCpUEKQqgqDnODjI7n96WvXmpHYTGhEfKsTnHGOeOeDWQDH0OKUpfd7mnduD8Ad2fs169fapHkiKPBKnO7jIG0entxS4+vtTTJ8GNQSpPnPdRhf4/2ayOW/s2gnvg80YIT5sfRR3H613Zrfgjv2LyX+v8AU1zrNoILiKJFBBVkyMAZ4wBgdzTnSnWGo9ObYbbwp7MyF5LdhgtkAHDdweKzOSeFHf0HrR8AcnJ9hR9mvToxsJlZKUtTZ3vTuqtE6ms5LWyu4lupIzus7jyyY9Rt/uH1XIrzzZS23/Ed1d2Wmi1to22pAZN/hkcHDH1PJ+naot1c3Ftr0F1aStFcwpvjkU8qwDEGndBklkhnldizyTFnY+pwCT/NUaenVdzxwOsnqqwzSvqKOpKxSAnvnaB+4Gag5zyc/oKQT+I5NFu/KtPgz1Wo8EChQoVVNUFHRUK44UKMUKFEgGHnmlAnGMnFChRIWwUdHQokAwCjoqFEAVd5/wA5h/7f+DU50+T8G4/6hP8AAoUKRH7z5jZfYLPNChQqyVz/2Q
                                            [parsed] => stdClass Object
                                                (
                                                    [source] => YouTube
                                                    [date] => Dec 20, 2018
                                                    [channel] => edureka!
                                                )

                                        )

                                    [1] => stdClass Object
                                        (
                                            [title] => Machine Learning Engineer Roadmap | Machine Learning ...
                                            [url] => https://www.youtube.com/watch?v=T4MLrtOKPjY
                                            [thumbnail] => data:image/jpeg;base64,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
                                            [parsed] => stdClass Object
                                                (
                                                    [source] => YouTube
                                                    [date] => May 11, 2021
                                                    [channel] => codebasics
                                                )

                                        )

                                    [2] => stdClass Object
                                        (
                                            [title] => 5 Top Skills Needed for Machine Learning Engineer | ML ...
                                            [url] => https://www.youtube.com/watch?v=NdqVOHDJGD8
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                            [title] => Essential Skills For Machine Learning Engineers | by Kurtis Pykes | Towards Data Science
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                            [description] => Do you enjoy creating software but are extremely intrigued by Data Science? If so, you may want to consider the role of a Machine Learning Engineer. Machine Learning engineers sit at the intersection…
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                                    [7] => Dashboard | HackerRank
                                    [8] => Kaggle: Your Machine Learning and Data Science Community
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                                    [14] => Kurtis Pykes
                                    [15] => More from Towards Data Science
                                    [16] => More From Medium
                                    [17] => Take your Machine Learning Models to Production with these 5 simple steps
                                    [18] => Deep Learning, the basics and more!
                                    [19] => Machine Learning Laboratory Protocols
                                    [20] => [Paper Reading] Learning to Hash for Indexing Big Data — A Survey
                                    [21] => Understanding Training output in TensorFlow
                                    [22] => Huawei ML FAQ Again
                                    [23] => Linear Regression Using Pytorch
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                                    [3] => Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data…
                                    [4] => Data science hackathons on DataHack enable you to compete with leading data scientists and machine learning experts in…
                                    [5] => And How I Overcame Them
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                            [body] => Essential Skills For Machine Learning EngineersBuilding a Machine Learning Engineer. Kurtis PykesMay 30, 2021·4 min readPhoto by ThisisEngineering RAEng on UnsplashDo you enjoy creating software but are extremely intrigued by Data Science? If so, you may want to consider the role of a Machine Learning Engineer. Machine Learning engineers sit at the intersection of Software Engineering and Data Science — meaning you’ll need both skills if you really want to excel.The focus of Data Scientists is to transform disparate data into actionable insights. On the other hand, the Machine Learning Engineer focuses on developing working software that makes use of the data as well as automating predictive models.Overview Of Data Related Roles. Data from Different Perspectives. Here’s a summary of the skills required:Software Engineering. Computer Science fundamentals are vital for the Machine Learning engineer; A good grasp of Data Structures & Algorithms such as Multi-dimensional arrays, arrays, stacks, queues, trees, etc. ML engineers should also be able to write algorithms that are capable of searching, sorting, and optimizing code. To further add, an understanding of computability, complexity, and computer architecture are all necessary.Since the final output from a Machine Learning engineer is typically deliverable software, ML engineers must have a good idea of how each of the different pieces of software works and communicates to build suitable interfaces for your component.Data Science. Data Scientists typically have a heavy reliance on programming languages such as Python, R, SQL, Java, etc. They also have a sound foundational base in probability and statistics — Topics include;Hypothesis TestingLikelihoodBayes RuleCalculusConditional ProbabilityHidden Markov ModelsDistributionsNote: This is by no means an exhaustive list. For more read my article, Courses to Learn Data Science in 2021Also, ML engineers should have adept data modeling and evaluation skills. Data modeling is the process of training a learning algorithm to predict the labels given a set of features.The goal of modeling is to identify useful patterns that best allow the model to generalize to new unseen instances — here is where evaluation comes into play. At the beginning of a Machine Learning project, the most appropriate evaluation metric for the task will be used to determine how well the algorithm has performed.Machine Learning. Many of the widely used Machine Learning algorithms can be implemented through third-party libraries such as Scikit-Learn, Keras, TensorFlow, PyTorch, MLlib, etc. However, applying these algorithms effectively includes selecting a model that is suitable for the problem at hand, an optimization method, and understanding the effect of hyperparameters on learning.Additionally, ML engineers should be adept at hyperparameter tuning. A hyperparameter is a parameter value that is used to control the learning process, therefore hyperparameter tuning may be described as the problem of selecting a set of optimal hyperparameters for a learning algorithm.Some other tools ML Engineers may be required to know (depending on the company they work for) include:Spark & HadoopApache KafkaGoogle Cloud ML EngineAmazon Machine LearningAzure Machine LearningIBM WatsonMachine Learning Engineer Self Learning Path. Like Data Science, there is a lot to learn to become a Machine Learning Engineer. Assuming we’ve already learned the foundation Math requirements, below I will leave some good resources to help you practice various parts of the ML engineer skillset:Software Engineering & ProgrammingProblems - LeetCode. Level up your coding skills and quickly land a job. This is the best place to expand your knowledge and get prepared…. leetcode.comDashboard | HackerRank. Join over 7 million developers in solving code challenges on HackerRank, one of the best ways to prepare for…. www.hackerrank.comData Science & Machine LearningKaggle: Your Machine Learning and Data Science Community. Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data…. www.kaggle.comDataHack : Biggest Data hackathon platform for Data Scientists. Data science hackathons on DataHack enable you to compete with leading data scientists and machine learning experts in…. datahack.analyticsvidhya.comCoursesMachine Learning by StandfordDeep Learning Specialization by DeepLearning.AIBecome a Machine Learning Engineer NanodegreeIntroduction To Machine Learning in ProductionComputer Science Principles by Khan AcademyAlgorithms By Khan AcademyWrap Up. As Data Science continues to shift from research to production, the demand for ML engineers has been increasing rapidly. If you have a knack for building great software but still love Data Science, the ML engineer path may be the one for you.Thank You for Reading!If you enjoyed this article, connect with me by subscribing to my FREE weekly newsletter. Never miss a post I make about Artificial Intelligence, Data Science, and Freelancing.My Biggest Challenges Being A Self-Taught Data Scientist. And How I Overcame Them. towardsdatascience.com5 Laws For Self-Taught Data Professionals. towardsdatascience.comData Scientists Should Know Software Engineering Best Practices. Becoming an indispensable Data Scientist. towardsdatascience.comKurtis Pykes. Follow52 52 52 Artificial IntelligenceEducationAdviceIdeasTechnologyMore from Towards Data Science. FollowYour home for data science. A Medium publication sharing concepts, ideas and codes.Read more from Towards Data ScienceMore From Medium. Take your Machine Learning Models to Production with these 5 simple steps. Rahul Agarwal in Towards Data ScienceDeep Learning, the basics and more!SelectStarMachine Learning Laboratory Protocols. Nwosu Rosemary in MLearning.ai[Paper Reading] Learning to Hash for Indexing Big Data — A Survey. Hayashi LynnUnderstanding Training output in TensorFlow. Gautam GoswamiHuawei ML FAQ Again. J3 in JungletronicsLinear Regression Using Pytorch. Shubhankar Nandakumar in The StartupRecommendation System: Content based (Part 1). Arsalaan Javed in Analytics Vidhya
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                            [body] => Top 30 Machine Learning Skills required to get a Machine Learning Job 15221 by Priyankur Sarkar 10th May, 2019 Last updated on 11th Mar, 2021 25 mins read Machine learning has been making a silent revolution in our lives since the past decade. From capturing selfies with a blurry background and focused face capture to getting our queries answered by virtual assistants such as Siri and Alexa, we are increasingly depending on products and applications that implement machine learning at their core.In more basic terms, machine learning is one of the steps involved in artificial intelligence. Machines learn through machine learning. How exactly? Just like how humans learn – through training, experience, and feedback.Once machines learn through machine learning, they implement the knowledge so acquired for many purposes including, but not limited to, sorting, diagnosis, robotics, analysis and predictions in many fields.It is these implementations and applications that have made machine learning an in-demand skill in the field of programming and technology.Look at the stats that show a positive trend for machine learning projects and careers.Gartner’s report on artificial intelligence showed that as many as 2.3 million jobs in machine learning would be available across the globe by 2020.Another study from Indeed, the online job portal giant, revealed that machine learning engineers, data scientists and software engineers with these skills are topping the list of most in-demand professionals.High profile companies such as Univa, Microsoft, Apple, Google, and Amazon have invested millions of dollars on machine learning research and designing and are developing their future projects on it.With so much happening around machine learning, it is no surprise that any enthusiast who is keen on shaping their career in software programming and technology would prefer machine learning as a foundation to their career. This post is specifically aimed at guiding such enthusiasts and gives comprehensive information on skills that are needed to become a machine learning engineer, who is ready to dive into the real-time challenges.Machine Learning Skills. Organizations are showing massive interest in using machine learning in their products, which would in turn bring plenty of opportunities for machine learning enthusiasts.When you ask machine learning engineers the question – “What do you do as a machine learning engineer?”, chances are high that individual answers would differ from one professional to another. This may sound a little puzzling, but yes, this is true!Hence, a beginner to machine learning needs to have a clear understanding that there are different roles that they can perform with machine learning skills. And accordingly the skill set that they should possess, would differ. This section will give clarity on machine learning skills that are needed to perform various machine learning roles.Broadly, three main roles come into the picture when you talk about machine learning skills:Data EngineerMachine Learning EngineerMachine Learning ScientistOne must understand that data science, machine learning and artificial intelligence are interlinked. The following quote explains this better:Data science produces insights. Machine learning produces predictions. Artificial intelligence produces actions.A machine learning engineer is someone who deals with huge volumes of data to train a machine and impart it with knowledge that it uses to perform a specified task. However, in practice, there may be a little more to add to this:Machine Learning RoleSkills RequiredRoles and ResponsibilitiesData EngineerPython, R, and DatabasesParallel and distributed Knowledge on quality and reliabilityVirtual machines and cloud environmentMapReduce and HadoopCleaning, manipulating and extracting the required data   Developing code for data analysis and manipulationPlays a major role in statistical analysis of dataMachine Learning EngineerConcepts of computer science and software engineeringData analysis and feature engineeringMetrics involved in MLML algorithm selection, and cross validationMath and StatisticsAnalyses and checks the suitability of an algorithm if it caters the needs of the current taskPlays main role in deciding and selecting machine learning libraries for given task.Machine Learning ScientistExpert knowledge in:Robotics and Machine LearningCognitive ScienceEngineeringMathematics and mathematical modelsDesigns new models and algorithms of machine learningResearches intensively on machine learning and publishes their research papers.Thus, gaining machine learning skills should be a task associated with clarity on the job role and of course the passion to learn them. As it is widely known, becoming a machine learning engineer is not a straightforward task like becoming a web developer or a tester.Irrespective of the role, a learner is expected to have solid knowledge on data science. Besides, many other subjects are intricately intertwined in learning machine learning and for a learner it requires a lot of patience and zeal to learn skills and build them up as they move ahead in their career.The following diagram shows the machine learning skills that are in demand year after year:AI - Artificial IntelligenceTensorFlowApache KafkaData ScienceAWS - Amazon Web Services                                                                                                                                                                                                                                                                                                                                Image SourceIn the coming sections, we would be discussing each of these skills in detail and how proficient you are expected to be in them.Technical skills required to become ML Engineer. Becoming a machine learning engineer means preparing oneself to handle interesting and challenging tasks that would change the way humanity is experiencing things right now. It demands both technical and non-technical expertise. Firstly, let’s talk about the technical skills needed for a machine learning engineer. Here is a list of technical skills a machine learning engineer is expected to possess:Applied MathematicsNeural Network ArchitecturesPhysicsData Modeling and EvaluationAdvances Signal Processing TechniquesNatural Language ProcessingAudio and video ProcessingReinforcement LearningLet us delve into each skill in detail now:1.Applied Mathematics. Mathematics plays an important role in machine learning, and hence it is the first one on the list. If you wish to see yourself as a proven machine learning engineer, you ought to love math and be an expert in the following specializations of math.But first let us understand why a machine learning engineer would need math at all? There are many scenarios where a machine learning engineer should depend on math. For example:Choosing the right algorithm that suits the final needsUnderstanding and working with parameters and their settings.Deciding on validation strategiesApproximating the confidence intervals.How much proficiency in Math does a machine learning engineer need to have?It depends on the level at which a machine learning engineer works. The following diagram gives an idea about how important various concepts of math are for a machine learning enthusiast.A) Linear algebra: 15%B) Probability Theory and Statistics: 25%C) Multivariate Calculus: 15%D) Algorithms and Optimization: 15%F) Other concepts: 10%Data SourceA) Linear Algebra. This concept plays a main role in machine learning. One has to be skilled in the following sub-topics of linear algebra:Principal Component Analysis (PCA), Singular Value Decomposition (SVD)Eigen decomposition of a matrixLU DecompositionQR Decomposition/FactorizationSymmetric MatricesOrthogonalization & OrthonormalizationMatrix OperationsProjectionsEigenvalues & EigenvectorsVector Spaces and NormsB) Probability Theory and Statistics. The core aim of machine learning is to reduce the probability of error in the final output and decision making of the machine. Thus, it is no wonder that probability and statistics play a major role.The following topics are important in these subjects:CombinatoricsProbability Rules & AxiomsBayes’ TheoremRandom VariablesVariance and ExpectationConditional and Joint DistributionsStandard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian)Moment Generating Functions, Maximum Likelihood Estimation (MLE)Prior and PosteriorMaximum a Posteriori Estimation (MAP)Sampling Methods.C) Calculus. In calculus, the following concepts have notable importance in machine learning:Integral CalculusPartial Derivatives,Vector-Values FunctionsDirectional GradientHessian, Jacobian, Laplacian and Lagrangian Distributions.D) Algorithms and Optimization. The scalability and the efficiency of computation of a machine learning algorithm depends on the chosen algorithm and optimization technique adopted. The following areas are important from this perspective:Data structures (Binary Trees, Hashing, Heap, Stack etc)Dynamic ProgrammingRandomized & Sublinear AlgorithmGraphsGradient/Stochastic DescentsPrimal-Dual methodsE) Other Concepts. Besides, the ones mentioned above, other concepts of mathematics are also important for a learner of machine learning. They are given below:Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions Limits, Cauchy Kernel, Fourier Transforms)Information Theory (Entropy, Information Gain)Function Spaces and Manifolds2.Neural Network Architectures. Neural networks are the predefined set of algorithms for implementing machine learning tasks. They offer a class of models and play a key role in machine learning.The following are the key reasons why a machine learning enthusiast needs to be skilled in neural networks:Neural networks let one understand how the human brain works and help to model and simulate an artificial one.Neural networks give a deeper insight of parallel computations and sequential computationsThe following are the areas of neural networks that are important for machine learning:Perceptrons Convolutional Neural Networks Recurrent Neural NetworkLong/Short Term Memory Network (LSTM)Hopfield Networks Boltzmann Machine NetworkDeep Belief NetworkDeep Auto-encoders3.Physics. Having an idea of physics definitely helps a machine learning engineer. It makes a difference in designing complex systems and is a skill that is a definite bonus for a machine learning enthusiast.4.Data Modeling and Evaluation. A machine learning has to work with huge amounts of data and leverage them into predictive analytics. Data modeling and evaluation is important in working with such bulky volumes of data and estimating how good the final model is.For this purpose, the following concepts are worth learnable for a machine learning engineer:Classification AccuracyLogarithmic LossConfusion MatrixArea under CurveF1 ScoreMean Absolute ErrorMean Squared Error5.Advanced Signal Processing Techniques. The crux of signal processing is to minimize noise and extract the best features of a given signal.For this purpose, it uses certain concepts such as:convex/greedy optimization theory and algorithmsspectral time-frequency analysis of signalsAlgorithms such as wavelets, shearlets, curvelets, contourlets, bandlets, etc.All these concepts find their application in machine learning as well.6. Natural language processing. The importance of natural language processing in artificial intelligence and machine learning is not to be forgotten. Various libraries and techniques of natural language processing used in machine learning are listed here:Gensim and NLTKWord2vecSentiment analysisSummarization7. Audio and Video Processing. This differs from natural language processing in the sense that we can apply audio and video processing on audio signals only. For achieving this, the following concepts are essential for a machine learning engineer:Fourier transformsMusic theoryTensorFlow8. Reinforcement Learning. Though reinforcement learning plays a major role in learning and understanding deep learning and artificial intelligence, it is good for a beginner of machine learning to know the basic concepts of reinforcement learning.Programming skills required to become ML Engineer. Machine learning, ultimately, is coding and feeding the code to the machines and getting them to do the tasks we intend them to do. As such, a machine learning engineer should have hands-on expertise in software programming and related concepts. Here is a list of programming skills a machine learning engineer is expected to have knowledge on:Computer Science Fundamentals and ProgrammingSoftware Engineering and System DesignMachine Learning Algorithms and LibrariesDistributed computingUnixLet us look into each of these programming skills in detail now:1.Computer Science Fundamentals and Programming. It is important that a machine learning engineer apply the concepts of computer science and programming correctly as the situation demands. The following concepts play an important role in machine learning and are a must on the list of the skillsets a machine learning engineer needs to have:Data structures (stacks, queues, multi-dimensional arrays, trees, graphs)Algorithms (searching, sorting, optimization, dynamic programming)Computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.)Computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.)2.Software Engineering and System Design. Whatever a machine learning engineer does, ultimately it is a piece of software code – a beautiful conglomerate of many essential concepts and the one that is entirely different from coding in other software languages.Hence, it is quintessential that a machine learning engineer have solid knowledge of the following areas of software programming and system design:Scaling algorithms with the size of dataBasic best practices of software coding and design, such as requirement analysis, version control, and testing.Communicating with different modules and components of work using library calls, REST APIs and querying through databases.Best measures to avoid bottlenecks and designing the final product such that it is user-friendly.3. Machine Learning Algorithms and Libraries. A machine learning engineer may need to work with multiple packages, libraries, algorithms as a part of day-to-day tasks. It is important that a machine learning engineer is well-versed with the following aspects of machine learning algorithms and libraries:A thorough idea of various learning procedures including linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods.Sound knowledge in packages and APIs such as scikit-learn, Theano, Spark MLlib, H2O, TensorFlow, etc.Expertise in models such as decision trees, nearest neighbor, neural net, support vector machine and a knack to deciding which one fits the best.Deciding and choosing hyperparameters that affect learning model and the outcome.Comfortable to work with concepts such as gradient descent, convex optimization, quadratic programming, partial differential equations.Select an algorithm which yields the best performance from random forests, support vector machines (SVMs), and Naive Bayes Classifiers, etc.4   Distributed Computing . Working as a machine learning engineer means working with huge sets of data, not just focused on one isolated system, but spread among a cluster of systems. For this purpose, it is important that a machine learning engineer knows the concepts of distributed computing.5. Unix. Most clusters and servers that machine learning engineers need to work are variants of Linux(Unix). Though randomly they work on Windows and Mac, more than half of the time, they need to work on Unix systems only. Hence having sound knowledge on Unix and Linux is a key skill to become a machine learning engineer.Programming Languages for Machine Learning. Machine learning engineers need to code to train machines. Several programming languages can be used to do this. The list of programming languages that a machine learning expert should essentially know are as under:C, C++ and JavaSpark and HadoopR ProgrammingApache KafkaPythonWeka PlatformMATLAB/OctaveIn this section, let us know in detail why each of these programming languages is important for a machine learning engineer:1.C, C++ and Java. These languages give essentials of programming and teach many concepts in a simple manner that form a foundation stone for working on complex programming patterns of machine learning. Knowledge of C++ helps to improve the speed of the program, while Java is needed to work with Hadoop and Hive, and other tools that are essential for a machine learning engineer.2.Spark and Hadoop. Hadoop skills are needed for working in a distributed computing environment. Spark, a recent variant of Hadoop is gaining popularity among the machine learning tribe. It is a framework to implement machine learning on a large scale.3.R Programming. R is a programming language built by statisticians specifically to work with programming that involves statistics. Many mathematical computations of machine learning are based on statistics; hence it is no wonder that a machine learning engineer needs to have sound knowledge in R programming.4.Apache Kafka. Apache Kafka concepts such as Kafka Streams and KSQL play a major role in pre-processing of data in machine learning. Also, a sound knowledge of Apache Kafka lets a machine learning engineer to design solutions that are both multi-cloud based or hybrid cloud-based.  Other concepts such as business information such as latency and model accuracy are also from Kafka and find use in Machine learning.5.Python. Of late, Python has become the unanimous programming language for machine learning. In fact, experts quote that humans communicate with machines through Python language.Why Python is preferred for Machine Learning?Python Programming Language has several key features and benefits that make it the monarch of programming languages for machine learning:It is an all-in-one purpose programming language that can do a lot more than dealing with statistics.It is beginner friendly and easy to learn.It boasts of rich libraries and APIs that solve various needs of machine learning pretty easily.Its productivity is higher than its other counterparts.It offers ease of integration and gets the workflow smoothly from the designing stage to the production stage.Python EcoSystem. There are various components of Python that make it preferred language for machine learning. Such components are discussed below:Jupyter NotebookNumpyPandasScikit-LearnTensorFlow1.Jupyter Notebook. Jupyter offers excellent computational environment for Python based data science applications. With the help of Jupyter notebook, a machine learning engineer can illustrate the flow of the process step-by-step very clearly.2.NumPy. NumPy or Numerical Python is one of the components of Python that allows the following operations of machine learning in a smooth way:Fourier transformationLinear algebraic operationsLogical and numerical operations on arrays.Of late, NumPy is gaining attention because it makes an excellent substitute to MATLAB, as it coordinates with Matplotlib and SciPy very smoothly.3.Pandas. Pandas is a Python library that offers various features for loading, manipulating, analysing, modeling and preparing data. It is entirely dedicated for data analysis and manipulation.4.Scikit-learn. Built on NumPy, SciPy, and Matplotlib, it is an open-source library of Python. It offers excellent features and functionalities for major aspects of machine learning such as clustering, dimensionality reduction, model reduction, regression and classification.5.TensorFlow. TensorFlow is another framework of Python. It finds its usage in deep learning and having a knowledge of its libraries such as Keras, helps a machine learning engineer to move ahead confidently in their career.6.Weka Platform. It is widely known that machine learning is a non-linear process that involves many iterations. Weka or Waikato Environment for Knowledge Analysis is a recent platform that is designed specifically designed for applied machine learning. This tool is also slowing gaining its popularity and thus is a must-include on the list of skills for a machine learning engineer.7.MATLAB/Octave. This is a basic programming language that was used for simulation of various engineering models. Though not popularly used in machine learning, having sound knowledge in MATLAB lets one learns the other mentioned libraries of Python easily.Soft skills or behavioural skills required to become ML engineer. Technical skills are relevant only when they are paired with good soft skills. And the machine learning profession is no exception to this rule. Here is a list of soft skills that a machine learning engineer should have:Domain knowledgeCommunication SkillsProblem-solving skillsRapid prototypingTime managementLove towards constant learningLet us move ahead and discuss how each of these skills make a difference to a machine learning engineer.1.Domain knowledge. Machine learning is such a subject that needs the best of its application in real-time. Choosing the best algorithm while solving a machine learning problem in your academia is far different from what you do in practice. Various aspects of business come into picture when you are a real-time machine learning engineer. Hence, a solid understanding of the business and domain of machine learning is of utmost importance to succeed as a good machine learning engineer.2.Communication Skills. As a machine learning engineer, you need to communicate with offshore teams, clients and other business teams. Excellent communication skills are a must to boost your reputation and confidence and to bring up your work in front of peers.3.Problem-solving skills. Machine learning is all about solving real time challenges. One must have good problem-solving skills and be able to weigh the pros and cons of the given problem and apply the best possible methods to solve it.4.Rapid Prototyping. Choosing the correct learning method or the algorithm are signs of a machine learning engineer’s good prototyping skills. These skills would be a great saviour in real time as they would show a huge impact on budget and time taken for successfully completing a machine learning project.5.Time management. Training a machine is not a cake-walk. It takes huge time and patience to train a machine. But it’s not always that machine learning engineers are allotted ample time for completing tasks. Hence, time management is an essential skill a machine learning professional should have to effectively deal with bottlenecks and deadlines.6.Love towards constant learning. Since its inception, machine learning has witnessed massive change – both in the way it is implemented and in its final form. As we have seen in the previous section, technical and programming skills that are needed for machine learning are constantly evolving. Hence, to prove oneself a successful machine learning expert, it is very crucial that they have a zeal to update themselves – constantly!Conclusion. The skills that one requires to begin their journey in machine learning are exactly what we have discussed in this post. The future for machine learning is undoubtedly bright with companies ready to offer millions of dollars as remuneration, irrespective of the country and the location.Machine learning and deep learning will create a new set of hot jobs in the next five years. – Dave WatersAll it takes to have an amazing career in machine learning is a strong will to hone one’s skills and gain a solid knowledge of them. All the best for an amazing career in machine learning! Priyankur Sarkar . Data Science Enthusiast Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations. Join the Discussion . Your email address will not be published. Required fields are marked * 4 comments . robin 21 Jun 2019 Your article had helped me a lot in learning indepth concepts of Machine learning,keep up the good work. Like Reply Rahul sharma 06 Aug 2019 The nice article very good and explained in an easy understanding way thanks to the author... Like Reply venkat k 06 Aug 2019 I have been surfing online more than 3 hours lately, yet I by no means discovered any interesting article like yours. Its beautiful value was sufficient for me. In my view, if all webmasters and bloggers made excellent content as you probably did, the internet can be a lot more helpful than ever before. Like Reply Bhavana 06 Aug 2019 One of my friend shared this article. I really loved the article and I started subscribing for Knowledgehut, please update me for the upcoming articles related to the machine learning... Like Reply Suggested Blogs. Regression Analysis And Its Techniques in Data Science. By Priyankur Sarkar . As a Data Science enthusiast, you might already know that a majority of business decisions these days are data-driven. However, it is essential to understand how to parse through all the data. One of the most important types of data analysis in this field is Regression Analysis. Regression Analysis is a form of predictive modeling technique mainly used in statistics. The term “regression” in this context, was first coined by Sir Francis Galton, a cousin of Sir Charles Darwin. The earliest form of regression was developed by Adrien-Marie Legendre and Carl Gauss - a method of least squares. Before getting into the what and how of regression analysis, let us first understand why regression analysis is essential. Why is regression analysis important? The evaluation of relationship between two or more variables is called Regression Analysis. It is a statistical technique.  Regression Analysis helps enterprises to understand what their data points represent, and use them wisely in coordination with different business analytical techniques in order to make better decisions. Regression Analysis helps an individual to understand how the typical value of the dependent variable changes when one of the independent variables is varied, while the other independent variables remain unchanged.  Therefore, this powerful statistical tool is used by Business Analysts and other data professionals for removing the unwanted variables and choosing only the important ones. The benefit of regression analysis is that it allows data crunching to help businesses make better decisions. A greater understanding of the variables can impact the success of a business in the coming weeks, months, and years in the future.  Data Science The regression method of forecasting, as the name implies, is used for forecasting and for finding the casual relationship between variables. From a business point of view, the regression method of forecasting can be helpful for an individual working with data in the following ways: Predicting sales in the near and long term. Understanding demand and supply. Understanding inventory levels. Review and understand how variables impact all these factors. However, businesses can use regression methods to understand the following: Why did the customer service calls drop in the past months? How the sales will look like in the next six months? Which ‘marketing promotion’ method to choose? Whether to expand the business or to create and market a new product. The ultimate benefit of regression analysis is to determine which independent variables have the most effect on a dependent variable. It also helps to determine which factors can be ignored and those that should be emphasized. Let us now understand what regression analysis is and its associated variables. What is regression analysis?According to the renowned American mathematician John Tukey, “An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem". This is precisely what regression analysis strives to achieve.  Regression analysis is basically a set of statistical processes which investigates the relationship between a dependent (or target) variable and an independent (or predictor) variable. It helps assess the strength of the relationship between the variables and can also model the future relationship between the variables. Regression analysis is widely used for prediction and forecasting, which overlaps with Machine Learning. On the other hand, it is also used for time series modeling and finding causal effect relationships between variables. For example, the relationship between rash driving and the number of road accidents by a driver can be best analyzed using regression.  Let us now understand regression with an example. Meaning of RegressionLet us understand the concept of regression with an example. Consider a situation where you conduct a case study on several college students. We will understand if students with high CGPA also get a high GRE score. Our first job is to collect the details of the GRE scores and CGPAs of all the students of a college in a tabular form. The GRE scores and the CGPAs are listed in the 1st and 2nd columns, respectively. To understand the relationship between CGPA and GRE score, we need to draw a scatter plot.  Here, we can see a linear relationship between CGPA and GRE score in the scatter plot. This indicates that if the CGPA increases, the GRE scores also increase. Thus, it would also mean that a student with a high CGPA is likely to have a greater chance of getting a high GRE score. However, if a question arises like “If the CGPA of a student is 8.51, what will be the GRE score of the student?”. We need to find the relationship between these two variables to answer this question. This is the place where Regression plays its role. In a regression algorithm, we usually have one dependent variable and one or more than one independent variable where we try to regress the dependent variable "Y" (in this case, GRE score) using the independent variable "X" (in this case, CGPA). In layman's terms, we are trying to understand how the value of "Y" changes concerning the change in "X". Let us now understand the concept of dependent and independent variables. Dependent and Independent variables In data science, variables refer to the properties or characteristics of certain events or objects. There are mainly two types of variables while performing regression analysis which is as follows: Independent variables – These variables are manipulated or are altered by researchers whose effects are later measured and compared. They are also referred to as predictor variables. They are called predictor variables because they predict or forecast the values of dependent variables in a regression model. Dependent variables – These variables are the type of variable that measures the effect of the independent variables on the testing units. It is safer to say that dependent variables are completely dependent on them. They are also referred to as predicted variables. They are called because these are the predicted or assumed values by the independent or predictor variables. When an individual is looking for a relationship between two variables, he is trying to determine what factors make the dependent variable change. For example, consider a scenario where a student's score is a dependent variable. It could depend on many independent factors like the amount of study he did, how much sleep he had the night before the test, or even how hungry he was during the test.  In data models, independent variables can have different names such as “regressors”, “explanatory variable”, “input variable”, “controlled variable”, etc. On the other hand, dependent variables are called “regressand,” “response variable”, “measured variable,” “observed variable,” “responding variable,” “explained variable,” “outcome variable,” “experimental variable,” or “output variable.” Below are a few examples to understand the usage and significance of dependent and independent variables in a wider sense: Suppose you want to estimate the cost of living of a person using a regression model. In that case, you need to take independent variables as factors such as salary, age, marital status, etc. The cost of living of a person is highly dependent on these factors. Thus, it is designated as the dependent variable. Another scenario is in the case of a student's poor performance in an examination. The independent variable could be factors, for example, poor memory, inattentiveness in class, irregular attendance, etc. Since these factors will affect the student's score, the dependent variable, in this case, is the student's score.  Suppose you want to measure the effect of different quantities of nutrient intake on the growth of a newborn child. In that case, you need to consider the amount of nutrient intake as the independent variable. In contrast, the dependent variable will be the growth of the child, which can be calculated by factors such as height, weight, etc. Let us now understand the concept of a regression line. What is the difference between Regression and Classification?Regression and Classification both come under supervised learning methods, which indicate that they use labelled training datasets to train their models and make future predictions. Thus, these two methods are often classified under the same column in machine learning.However, the key difference between them is the output variable. In regression, the output tends to be numerical or continuous, whereas, in classification, the output is categorical or discrete in nature.  Regression and Classification have certain different ways to evaluate the predictions, which are as follows: Regression predictions can be interpreted using root mean squared error, whereas classification predictions cannot.  Classification predictions can be evaluated using accuracy, whereas, on the other hand, regression predictions cannot be evaluated using the same. Conclusively, we can use algorithms like decision trees and neural networks for regression and classification with small alterations. However, some other algorithms are more difficult to implement for both problem types, for example, linear regression for regressive predictive modeling and logistic regression for classification predictive modeling. What is a Regression Line?In the field of statistics, a regression line is a line that best describes the behaviour of a dataset, such that the overall distance from the line to the points (variable values) plotted on a graph is the smallest. In layman's words, it is a line that best fits the trend of a given set of data.  Regression lines are mainly used for forecasting procedures. The significance of the line is that it describes the interrelation of a dependent variable “Y” with one or more independent variables “X”. It is used to minimize the squared deviations of predictions.  If we take two variables, X and Y, there will be two regression lines: Regression line of Y on X: This gives the most probable Y values from the given values of X. Regression line of X on Y: This gives the most probable values of X from the given values of Y. The correlation between the variables X and Y depend on the distance between the two regression lines. The degree of correlation is higher if the regression lines are nearer to each other. In contrast, the degree of correlation will be lesser if the regression lines are farther from each other.  If the two regression lines coincide, i.e. only a single line exists, correlation tends to be either perfect positive or perfect negative. However, if the variables are independent, then the correlation is zero, and the lines of regression will be at right angles.  Regression lines are widely used in the financial sector and business procedures. Financial Analysts use linear regression techniques to predict prices of stocks, commodities and perform valuations, whereas businesses employ regressions for forecasting sales, inventories, and many other variables essential for business strategy and planning. What is the Regression Equation? In statistics, the Regression Equation is the algebraic expression of the regression lines. In simple terms, it is used to predict the values of the dependent variables from the given values of independent variables.  Let us consider one regression line, say Y on X and another line, say X on Y, then there will be one regression equation for each regression line: Regression Equation of Y on X: This equation depicts the variations in the dependent variable Y from the given changes in the independent variable X. The expression is as follows: Ye = a + bX Where,  Ye is the dependent variable, X is the independent variable, a and b are the two unknown constants that determine the position of the line. The parameter “a” indicates the distance of a line above or below the origin, i.e. the level of the fitted line, whereas parameter "b" indicates the change in the value of the independent variable Y for one unit of change in the dependent variable X. The parameters "a" and "b" can be calculated using the least square method. According to this method, the line needs to be drawn to connect all the plotted points. In mathematical terms, the sum of the squares of the vertical deviations of observed Y from the calculated values of Y is the least. In other words, the best-fitted line is obtained when ∑ (Y-Ye)2 is the minimum. To calculate the values of parameters “a” and “b”, we need to simultaneously solve the following algebraic equations: ∑ Y = Na + b ∑ X ∑ XY = a ∑ X + b ∑ X2 Regression Equation of X on Y: This equation depicts the variations in the independent variable Y from the given changes in the dependent variable X. The expression is as follows: Xe = a + bY  Where,  Xe is the dependent variable, Y is the independent variable, a and b are the two unknown constants that determine the position of the line. Again, in this equation, the parameter “a” indicates the distance of a line above or below the origin, i.e. the level of the fitted line, whereas parameter "b" indicates the slope, i.e. change in the value of the dependent variable X for a unit of change in the independent variable Y. To calculate the values of parameters “a” and “b” in this equation, we need to simultaneously solve the following two normal equations: ∑ X = Na + b ∑ Y ∑ XY = a ∑ Y + b ∑ Y2 Please note that the regression lines can be completely determined only if we obtain the constant values “a” and “b”. How does Linear Regression work?Linear Regression is a Machine Learning algorithm that allows an individual to map numeric inputs to numeric outputs, fitting a line into the data points. It is an approach to modeling the relationship between one or more variables. This allows the model to able to predict outputs. Let us understand the working of a Linear Regression model using an example. Consider a scenario where a group of tech enthusiasts has created a start-up named Orange Inc. Now, Orange has been booming since 2016. On the other hand, you are a wealthy investor, and you want to know whether you should invest your money in Orange in the next year or not. Let us assume that you do not want to risk a lot of money, so you buy a few shares. Firstly, you study the stock prices of Orange since 2016, and you see the following figure: It is indicative that Orange is growing at an amazing rate where their stock price has gone from 100 dollars to 500 dollars in only three years. Since you want your investment to boom along with the company's growth, you want to invest in Orange in the year 2021. You assume that the stock price will fall somewhere around $500 since the trend will likely not go through a sudden change. Based on the information available on the stock prices of the last couple of years, you were able to predict what the stock price is going to be like in 2021.  You just inferred your model in your head to predict the value of Y for a value of X that is not even in your knowledge. This mental method you undertook is not accurate anyway because you were not able to specify what exactly will be the stock price in the year 2021. You just have an idea that it will probably be above 500 dollars. This is where Regression plays its role. The task of Regression is to find the line that best fits the data points on the plot so that we can calculate where the stock price is likely to be in the year 2021.  Let us examine the Regression line (in red) by understanding its significance. By making some alterations, we obtained that the stock price of Orange is likely to be a little higher than 600 dollars by the year 2021. This example is quite oversimplified, so let us examine the process and how we got the red line on the next plot. Training the Regressor The example mentioned above is an example of Univariate Linear Regression since we are trying to understand the change in an independent variable X to one dependent variable, Y. Any regression line on a plot is based on the formula: f(X) = MX + B  Where, M is the slope of the line, B is the y-intercept that allows the vertical movement of the line, And X is the function’s input variable. In the field of Machine Learning, the formula is as follows: h(X) = W0 + W1X  Where, W0 and W1 are the weights, X is the input variable, h(X) is the label or the output variable. Regression works by finding the weights W0 and W1 that lead to the best-fitting line for the input variable X. The best-fitted line is obtained in terms of the lowest cost. Now, let us understand what does cost means here. The cost functionDepending upon the Machine Learning application, the cost could take different forms. However, in a generalized view, cost mainly refers to the loss or error that the regression model yields in its distance from the original training dataset. In a Regression model, the cost function is the Squared Error Cost: J(W0,W1) = (1/2n) Σ { (h(Xi) - Ti)2} for all i =1 until i = n    Where, J(W0, W1) is the total cost of the model with weights W0 and W1, h(Xi) is the model’s prediction of the independent variable Y at feature X with index  i, Ti is the actual y-value at index i, and n refers to the total number of data points in the dataset. The cost function is used to obtain the distance between the y-value the model predicted and the actual y-value in the data set. Then, the function squares this distance and divides it by the number of data points, resulting in the average cost. The 2 in the term ‘(1/2n)’ is merely to make the differentiation process in the cost function easier.  Training the dataset Training a regression model uses a Learning Algorithm to find the weights W0 and W1 that will minimize the cost and plug them into the straight-line function to obtain the best-fitted line. The pseudo-code for the algorithm is as follows: Repeat until convergence {      temp0 := W0 - a.((d/dW0) J(W0,W1))      temp1 := W1 - a.((d/dW1) J(W0,W1))      W0 = temp0      W1 = temp1  } Here, (d/dW0) and (d/dW1) refer to the partial derivatives of J(W0,, W1) concerning W0, and W1 respectively.  The gist of the partial differentiation is basically the derivatives: (d/dW0) J(W0,W1) = W0 + W1.X - T (d/dW1) j(W0,W1) = (W0 + W1.X - T).X Implementing the Gradient Descent Learning algorithm will result in a model with minimum cost. The weights that led to the minimum cost are dealt with as the final values for the line function h(X) = W0 + W1X.  Goodness-of-Fit in a Regression Model The Regression Analysis is a part of the linear regression technique. It examines an equation that lessens the distance between the fitted line and all data points. Determining how well the model fits the data is crucial in a linear model. The general idea is that if the deviations between the observed values and the predicted values of the linear model are small and unbiased, the model has well-fit data.  In technical terms, “Goodness-of-fit” is a mathematical model describing the differences between the observed and expected values or how well the model fits a set of observations. This measure can be used in statistical hypothesis testing.How do businesses use Regression Analysis? Regression Analysis is a statistical technique used to evaluate the relationship between two or more independent variables. Organizations use regression analysis to understand the significance of their data points and use analytical techniques to make better decisions.Business Analysts and Data Professionals use this statistical tool to delete unwanted variables and select the significant ones. There are numerous ways that businesses use regression analysis. Let us discuss some of them below. 1. Decision-makingBusinesses need to make better decisions to run smoothly and efficiently, and it is also necessary to understand the effects of the decision taken. They collect data on various factors such as sales, investments, expenditures, etc. and analyze them for further improvements. Organizations use the Regression Analysis method by making sense of the data and gathering meaningful insights. Business analysts and data professionals use this method to make strategic business decisions.2. Optimization of business The main role of regression analysis is to convert the collected data into actionable insights. The old-school techniques like guesswork and assuming a hypothesis have been eliminated by organizations. They are now focusing on adopting data-driven decision-making techniques, which improves the work performance in an organization. This analysis helps the management sectors in an organization to take practical and smart decisions. The huge volume of data can be interpreted and understood to gain efficient insights. 3. Predictive Analysis Businesses make use of regression analysis to find patterns and trends. Business Analysts build predictions about future trends using historical data. Regression methods can also go beyond predicting the impact on immediate revenue. Using this method, you can forecast the number of customers willing to buy a service and use that data to estimate the workforce needed to run that service. Most insurance companies use regression analysis to calculate the credit health of their policyholders and the probable number of claims in a certain period. Predictive Analysis helps businesses to: Minimize costs Minimize the number of required tools Provide fast and efficient results Detect fraud Risk Management Optimize marketing campaigns 4. Correcting errors Regression Analysis is not only used for predicting trends, but it is also useful to identify errors in judgements. Let us consider a situation where the executive of an organization wants to increase the working hours of its employees and make them work extra time to increase the profits. In such a case, regression analysis analyses all the variables and it may conclude that an increase in the working hours beyond their existing time of work will also lead to an increase in the operation expense like utilities, accounting expenditures, etc., thus leading to an overall decrease in the profit.   Regression Analysis provides quantitative support for better decision-making and helps organizations minimize mistakes. 5. New Insights Organizations generate a large amount of cluttered data that can provide valuable insights. However, this vast data is useless without proper analysis. Regression analysis is responsible for finding a relationship between variables by discovering patterns not considered in the model. For example, analyzing data from sales systems and purchase accounts will result in market patterns such as increased demand on certain days of the week or at certain times of the year. You can maintain optimal stock and personnel using the information before a demand spike arises. The guesswork gets eliminated by data-driven decisions. It allows companies to improve their business performance by concentrating on the significant areas with the highest impact on operations and revenue. Use cases of Regression AnalysisPharmaceutical companies Pharmaceutical organizations use regression analysis to analyze the quantitative stability data for the retest period or estimate shelf life. In this method, we find the nature of the relationship between an attribute and time. We determine whether the data should be transformed for linear regression analysis or non-linear regression analysis using the analyzed data. FinanceThe simple linear regression technique is also called the Ordinary Least Squares or OLS method. This method provides a general explanation for placing the line of the best fit among the data points.  This particular tool is used for forecasting and financial analysis. You can also use it with the Capital Asset Pricing Model (CAPM), which depicts the relationship between the risk of investing and the expected return. Credit Card Credit card companies use regression analysis to analyze various factors such as customer's risk of credit default, prediction of credit balance, expected consumer behaviour, and so on. With the help of the analyzed information, the companies apply specific EMI options and minimize the default among risky customers. When Should I Use Regression Analysis? Regression Analysis is mainly used to describe the relationships between a set of independent variables and the dependent variables. It generates a regression equation where the coefficients correspond to the relationship between each independent and dependent variable.  Analyze a wide variety of relationships You can use the method of regression analysis to perform many things, for example: To model multiple independent variables. Include continuous and categorical variables. Use polynomial terms for curve fitting. Evaluate interaction terms to examine whether the effect of one independent variable is dependent on the value of another variable.  Regression Analysis can untangle very critical problems where the variables are entwined. Consider yourself to be a researcher studying any of the following: What impact does socio-economic status and race have on educational achievement? Do education and IQ affect earnings? Impact of exercise habits and diet affect weight. Do drinking coffee and smoking cigarettes reduce the mortality rate? Does a particular exercise have an impact on bone density? These research questions create a huge amount of data that entwines numerous independent and dependent variables and question their influence on each other. It is an important task to untangle this web of related variables and find out which variables are statistically essential and the role of each of these variables. To answer all these questions and rescue us in this game of variables, we need to take the help of regression analysis for all the scenarios. Control the independent variables Regression analysis describes how the changes in each independent variable are related to the changes in the dependent variable and how it is responsible for controlling every variable in a regression model. In the process of regression analysis, it is crucial to isolate the role of each variable. Consider a scenario where you participated in an exercise intervention study. You aimed to determine whether the intervention was responsible for increasing the subject's bone mineral density. To achieve an outcome, you need to isolate the role of exercise intervention from other factors that can impact the bone density, which can be the diet you take or any other physical activity. To perform this task, you need to reduce the effect of the unsupportive variables. Regression analysis estimates the effect the change in one dependent variable has on the dependent variables while all other independent variables are constant. This particular process allows you to understand each independent variable's role without considering the other variables in the regression model. Now, let us understand how regression can help control the other variables in the process. According to a recent study on the effect of coffee consumption on mortality, the initial results depicted that the higher the intake of coffee, the higher is the risk of death. However, researchers did not include the fact that most coffee drinkers smoke in their first model. After smoking was included in the model, the regression results were quite different from the initial results. It depicted that coffee intake lowers the risk of mortality while smoking increases it. This model isolates the role of each variable while holding the other variables constant. You can examine the effect of coffee intake while controlling the smoking factor. On the other hand, you can also look at smoking while controlling for coffee intake. This particular example shows how omitting a significant variable can produce misleading results and causes it to be uncontrolled. This warning is mainly applicable for observational studies where the effects of omitted significant variables can be unbalanced. This omitted variable bias can be minimized in a randomization process where true experiments tend to shell out the effects of these variables in an equal manner. What are Residuals in Regression Analysis? Residuals identify the deviation of observed values from the expected values. They are also referred to as error or noise terms. It gives an insight into how good our model is against the actual value, but there are no real-life representations of residual values. Calculating the real values of intercept, slope, and residual terms can be a complicated task. However, the Ordinary Least Square (OLS) regression technique can help us speculate on an efficient model.  The technique minimizes the sum of the squared residuals. With the help of the residual plots, you can check whether the observed error is consistent with stochastic error (differences between the expected and observed values must be random and unpredictable). What are the Linear model assumptions in Regression Analysis? Regression Analysis is the first step in the process of predictive modeling. It is quite easy to implement, and its syntax and parameters do not create any kind of confusion. However, the purpose of regression analysis is not just solved by running a single line of code. It is much more than that. The function plot(model_name) returns four plots in the R programming language. Each of these plots provides essential information about the dataset. Most beginners in the field are unable to trace the information. But once you understand these plots, you can bring important improvements to your regression model. For significant improvements in your regression model, it is also crucial to understand the assumptions you need to take in your model and how you can fix them if any assumption gets violated. The four assumptions that should be met before conducting linear regression are as follows:  Linear Relationship: A linear relationship exists between the independent variable, x, and the dependent variable, y.   Independence: The residuals in linear regression are independent. In other words, there is no correlation between consecutive residuals in time series data.  Homoscedasticity: Residuals have constant variance at every level of X.  Normality: The residuals of the model are normally distributed. Assumption 1: Linear Relationships Explanation The first assumption in Linear regression is that there is a linear relationship between the independent variable X and the dependent variable Y. How to determine if this assumption is met The quickest and easiest way to detect this assumption is by creating a scatter plot of X vs Y. By looking at the scatter plot, you can have a visual representation of the linear relationship between the two variables. If the points in the plot could fall along a straight line, then there exists some type of linear relationship between the variables, and this assumption is met. For example, consider this first plot below. The points in the plot look like they fall roughly on a straight line, which indicates that there exists a linear relationship between X and Y: However, there doesn’t appear to be a linear relationship between X and Y in this second plot below:  And in this third plot, there appears to be a clear relationship between X and Y, but a linear relationship between:What to do if this assumption is violated If you create a scatter plot between X and Y and do not find any linear relationship between the two variables, then you can do two things: You can apply a non-linear transformation to the dependent or independent variables. Common examples might include taking the log, the square root, or the reciprocal of the independent and dependent variable. You can add another independent variable to the regression model. If the plot of X vs Y has a parabolic shape, then adding X2 as an additional independent variable in the linear regression model might make sense. Assumption 2: Independence Explanation The second assumption of linear regression is that the residuals should be independent. Its relevance can be seen while working with time-series data. In an ideal manner, a pattern among consecutive residuals is not what we want. For example, in a time series model, the residuals should not grow steadily along with time.  How to determine if this assumption is met To determine if this assumption is met, we need to have a scatter plot of residuals vs time and look at the residual time series plot. In an ideal plot, the residual autocorrelations should fall within the 95% confidence bands around zero, located at about +/- 2-over the square root on n, where n denotes the sample size.  You can also perform the Durbin-Watson test to formally examine if this assumption is met. What to do if this assumption is violated If this assumption is violated, you can do three things which are as follows: If there is a positive serial correlation, you can add lags of the independent variable or dependent variable to the regression model.  If there is a negative serial correlation, check that none of the variables has differences.  If there is a seasonal correlation, consider adding a seasonal dummy variable into your regression model.  Assumption 3: HomoscedasticityExplanation  The third assumption of linear regression is that the residuals should have constant variance at every level of X. This property is called homoscedasticity. When homoscedasticity is not present, the residuals suffer from heteroscedasticity. The outcome of the regression analysis becomes hard to trust when heteroscedasticity is present in the model. It increases the variance of the regression coefficient estimates, but the model does not recognize this fact. This makes the model declare that a term in the model is significantly crucial, but it is not. How to determine if this assumption is met To determine if this assumption is met, we need to have a scatter plot of fitted values vs residual plots. To achieve this, you need to fit a regression line into a data set.  Below is a scatterplot showing a typical fitted value vs residual plot in which heteroscedasticity is present: You can observe how the residuals become much more spread out as the fitted values get larger. The “cone” shape is a classic sign of heteroscedasticity:  What to do if this assumption is violated If this assumption is violated, you can do three things which are as follows: Transform the dependent variable: The most common transformation is simply taking the dependent variable's log. Consider if you are using population size as an independent variable to predict the number of flower shops in a city as the dependent variable. You need to use population size to predict the number of flower shops in a city. It causes heteroscedasticity to go away.  Redefine the dependent variable: One common way is to use a rate rather than the raw value. Consider the previous example. In that case, use population size to predict the number of flower shops per capita instead. This reduces the variability that naturally occurs among larger populations.  Use weighted regression: The third way to fix heteroscedasticity is to use weighted regression. In this regression method, we assign a weight to each data point depending on the variance of its fitted value, giving small weights to data points having higher variances, which shrinks their squared residuals. When the proper weights are used, the problem of heteroscedasticity gets eradicated. Assumption 4: Normality Explanation We need to take the last assumption that the residuals should be normally distributed. How to determine if this assumption is met To determine if this assumption is met, there are two common ways to achieve that: 1. Use Q-Q plots to examine the assumption visually. Also known as the quantile-quantile plot, it is used to determine whether or not the residuals of the regression model follow a normal distribution. The normality assumption is achieved if the points on the plot roughly form a straight diagonal line as follows: However, this Q-Q plot below shows when the residuals clearly deviate from a straight diagonal line, they do not follow a normal distribution:  2. Some other formal statistical tests to check the normality assumption are Shapiro-Wilk, Kolmogorov-Smirnov, Jarque-Barre, and D'Agostino-Pearson.  These tests however have a limitation as they are used only when there are large sample sizes and it often results that the residuals are not normal. Therefore, graphical techniques like Q-Q plots are easier to check the normality assumption and are also more preferable. What to do if this assumption is violatedIf this assumption is violated, you can do two things which are as follows: Firstly, examine if outliers are present and exist, make sure they are real values and aren’t data errors. Also, verify that any outliers aren’t having a large impact on the distribution. Secondly, you can apply a non-linear transformation to the independent and/or dependent variables. Common examples include taking the log, the square root, or the reciprocal of the independent and/or dependent variable. How to perform a simple linear regression?The formula for a simple linear regression is: Y = B0 + B1X + e Where, Y refers to the predicted value of the dependent variable Y for any given value of the independent variable X. B0 denotes the intercept, i.e. the predicted value of y when the x is 0. B1 denotes the regression coefficient, i.e. how much we expect the value of y to change as the value of x increases. X refers to the independent variable, or the variable we expect is influencing y). e denotes the error estimate, i.e. how much variation exists in our regression coefficient estimate. The Linear regression model's task is to find the best-fitted line through the data by looking out for the regression coefficient B1 that minimizes the total error estimate e of the model. Simple linear regression in R R is a free statistical programming language that most data professionals use very powerful and widely. Let us consider a dataset of income and happiness that we will use to perform regression analysis.The first task is to load the income.data dataset into the R environment, and then generate a linear model describing the relationship between income and happiness by the command as follows: income.happiness.lm | t |) column displays the p-value, which tells us how probable we are to see the estimated effect of income on happiness considering the null hypothesis of no effect were true. We can reject the null hypothesis since the p-value is very low (p < 0.001), and finally, we can conclude that income has a statistically crucial effect on happiness. The most important thing here in the linear regression model is the p-value. In this example, it is quite significant (p < 0.001), which shows that this model is a good fit for the observed data. Presenting the results While presenting your results, you should include the regression coefficient, standard error of the estimate, and the p-value. You should also interpret your numbers so that readers can have a clear understanding of the regression coefficient: A significant relationship (p < 0.001) has been found between income and happiness (R2 = 0.71 ± 0.018), with a 0.71-unit increase in reported happiness for every $10,000 increase in income. For a simple linear regression, you can simply plot the observations on the x and y-axis of a scatter plot and then include the regression line and regression function.What is multiple regression analysis?Multiple Regression is an extension of simple linear regression and is used to estimate the relationship between two or more independent variables and one dependent variable. You can perform multiple regression analysis to know: The strength of the relationship between one or more independent variables and one dependent variable. For example, you can use it to understand whether the exam performance can be predicted based on revision time, test anxiety, lecture attendance, and gender.  The overall fit, i.e. variance of the model and the relative impact of each of the predictors to the total variance explained. For example, you might want to know how much of the variation in the student’s exam performance can be understood by revision time, test anxiety, lecture attendance, gender, and the relative impact of each independent variable in explaining the variance. How to perform multiple linear regression? The formula for multiple linear regression is: Y = B0 + B1X1 + … + BnXn + e Where, Y refers to the predicted value of the dependent variable Y for any given value of the independent variable X. B0 denotes the intercept, i.e. the predicted value of y when the x is 0. B1X1  denotes the regression coefficient (B1), i.e. how much we expect the value of Y to change as the value of X increases. ... does the same for all the independent variables we want to test. BnXn refers to the regression coefficient of the last independent variable e denotes the error estimate of the model, i.e. how much variation exists in our estimate of the regression coefficient. It is the task of the Multiple Linear regression model to find the best-fitted line through the data by calculating the following three things: The regression coefficients will lead to the least error in the overall multiple regression model. The t-statistic of the overall regression model. The associated p-value  The multiple regression model also calculates the t-statistic and p-value for each regression coefficient. Multiple linear regression in R Let us consider a dataset of the heart and other factors that affect the functioning of our heart to perform multiple regression analyses. The first task is to load the heart.data dataset into the R environment, and then generate a linear model describing the relationship between heart disease and biking to work by the command as follows: heart.disease.lm| t |) column displays the p-value, which tells us how probable we are to see the estimated effect of income on happiness considering the null hypothesis of no effect were true. We can reject the null hypothesis since the p-value is very low (p < 0.001), and finally, we can conclude that both - biking to work and smoking - have influenced rates of heart disease. The most important thing here in the linear regression model is the p-value. In this example, it is quite significant (p < 0.001), which shows that this model is a good fit for the observed data. Presenting the results While presenting your results, you should include the regression coefficient, standard error of the estimate, and the p-value. You should also interpret your numbers in the proper context so that readers can have a clear understanding of the regression coefficient:  In our survey of 500 towns, we found significant relationships between the frequency of biking to work and the frequency of heart disease and the frequency of smoking and heart disease (p < 0.001 for each). Specifically, we found a 0.2% decrease (± 0.0014) in the frequency of heart disease for every 1% increase in biking and a 0.178% increase (± 0.0035) in the frequency of heart disease for every 1% increase in smoking. For multiple linear regression, you can simply plot the observations on the X and Y-axis of a scatter plot and then include the regression line and regression function: In this example, we have calculated the predicted values of the dependent variable heart disease across the observed values for the percentage of people biking to work. However, to include the effect of smoking on the independent variable heart disease, we had to calculate the predicted values by holding the variable smoking as constant at the minimum, mean, and maximum observed smoking rates. What is R-squared in Regression Analysis? In data science, R-squared (R2) is the coefficient of determination or the coefficient of multiple determination in case of multiple regression.  In the linear regression model, R-squared acts as an evaluation metric to evaluate the scatter of the data points around the fitted regression line. It recognizes the percentage of variation of the dependent variable. R-squared and the Goodness-of-fit R-squared is the proportion of variance in the dependent variable that the independent variable can explain.The value of R-squared stays between 0 and 100%: 0% corresponds to a model that does not explain the variability of the response data around its mean. The mean of the dependent variable helps predict the dependent variable and the regression model. On the other hand, 100% corresponds to a model that explains all the variability of the response variable around its mean. If your value of R2  is large, you have a better chance of your regression model fitting the observations.Although you get essential insights about the regression model in this statistical measure, you should not depend on it for the complete assessment of the model. It lacks information about the relationship between the dependent and the independent variables. It also does not inform about the quality of the regression model. Hence, as a user, you should always analyze R2 and other variables and then derive conclusions about the regression model. Visual Representation of R-squared You can visually demonstrate the plots of fitted values by observed values in a graphical manner. It illustrates how R-squared values represent the scatter around the regression line.  As observed in the pictures above, the value of R-squared for the regression model on the left side is 17%, and for the model on the right is 83%. When the variance accounts to be high in a regression model, the data points tend to fall closer to the fitted regression line.  However, a regression model with an R2 of 100% is an ideal scenario that is impossible. In such a case, the predicted values equal the observed values, leading all the data points to fall exactly on the regression line.  Interpretation of R-squared The simplest interpretation of R-squared is how good the regression model fits the observed data values. Let us loot at an example to understand this. Consider a model where the  R2  value is 70%. This would mean that the model explains 70% of the fitted data in the regression model. Usually, when the R2  value is high, it suggests a better fit for the model. The correctness of the statistical measure does not only depends on R2. Still, it can depend on other several factors like the nature of the variables, the units on which the variables are measured, etc. So, a high R-squared value is not always likely for the regression model and can indicate problems too.A low R-squared value is a negative indicator for a model in general. However, if we consider the other factors, a low R2 value can also result in a good predictive model. Calculation of R-squared R- squared can be evaluated using the following formula:  Where: SSregression – Explained sum of squares due to the regression model. SStotal – The total sum of squares. The sum of squares due to regression assesses how well the model represents the fitted data. The total sum of squares measures the variability in the data used in the regression model.Now let us come back to the earlier situation where we have two factors: the number of hours of study per day and the score in a particular exam to understand the calculation of R-squared more effectively. Here, the target variable is represented by score and the independent variable by the number of study hours per day.  In this case, we will need a simple linear regression model and the equation of the model will be as follows:  ŷ = w1x1 + b  The parameters w1 and b can be calculated by reducing the squared error over all the data points. The following equation is called the least square function:minimize ∑(yi –  w1x1i – b) Now, R-squared calculates the amount of variance of the target variable explained by the model, i.e. function of the independent variable. However, to achieve that, we need to calculate two things: Variance of the target variable: var(avg) = ∑(yi – Ӯ)2 The variance of the target variable around the best-fit line: var(model) = ∑(yi – ŷ)2Finally, we can calculate the equation of R-squared as follows:  R2 = 1 – [var(model)/var(avg)] = 1 -[∑(yi – ŷ)2/∑(yi – Ӯ)2]    What are the different types of regression analysis?   Other than simple linear regression and multiple linear regression, there are mainly 5 types of regression techniques. Let us discuss them one by one.  Polynomial RegressionIn a polynomial regression technique, the power of the independent variable has to more than 1. The expression below shows a polynomial equation: y = a + bx2  In this regression technique, the best-fitted line is a curve line instead of a straight line that fits into the data points. An important point to keep in mind while performing polynomial regression is, if you try to fit a polynomial of a higher degree to get a lower error, it might result in overfitting.  You should always plot the relationships to see the fit and always make sure that the curve fits the nature of the problem. An example to illustrate how plotting can help: Logistic Regression The logistic regression technique is used when the dependent variable is discrete in nature. For example, 0 or 1, true or false, etc. The target variable in this regression can have only two values and the relation between the target variable and the independent variable is denoted by a sigmoid curve. To measure the relationship between the target variable and independent variables,  Logit function is used. The expression below shows a logistic equation: logit(p) = ln(p/(1-p)) = b0 + b1X1 + b2X2 + b3X3 …. + bkXk Where,  p denotes the probability of occurrence of the feature. Ridge Regression The Ridge Regression technique is usually used when there is a high correlation between the independent variables. This is because the least square estimates result in unbiased values when there are multi collinear data.  However, if the collinearity is very high, there exists some bias value. Therefore, it is crucial to introduce a bias matrix in the equation of Ridge Regression. This regression method is quite powerful where the model is less susceptible to overfitting. The expression below shows a ridge regression equation: β = (X^{T}X + λ*I)^{-1}X^{T}y The lambda (λ) in the equation solves the issue of multicollinearity. Lasso Regression Lasso Regression is one of the types of regression in machine learning that is responsible for performing regularization and feature selection. It restricts the absolute size of the regression coefficient, due to which the coefficient value gets nearer to zero.The feature selection method in Lasso Regression allows the selection of a set of features from the dataset to build the model. Only the required features are used in this regression, while others are made zero. This helps in avoiding overfitting in the model.  If the independent variables are highly collinear, then this regression technique takes only one variable and makes other variables shrink to zero. The expression below shows a lasso regression equation: N^{-1}Σ^{N}_{i=1}f(x_{i}, y_{I}, α, β) Bayesian RegressionIn the Bayesian Regression method, the Bayes theorem is used to determine the value of regression coefficients. In this linear regression technique, the posterior distribution of the features is evaluated other than finding the least-squares.  Bayesian Linear Regression collaborates with Linear Regression and Ridge Regression but is more stable than simple Linear Regression. What are the terminologies used in Regression Analysis? When trying to understand the outcome of regression analysis, it is important to understand the key terminologies used to acknowledge the information.  A comprehensive list of regression analysis terms used are described below: Estimator: An estimator is an algorithm for generating estimates of parameters when the relevant dataset is present. Bias: An estimate is said to be unbiased when its expectation is the same as the value of the parameter that is being estimated. On the other hand, if the expectation is the same as the value of the estimated parameter, it is said to be biased. Consistency: An estimator is consistent if the estimates it produces converge on the value of the true parameter considering the sample size increases without limit. For example, an estimator that produces estimates θ^ for some value of parameter θ, where ^ is a small number. If the estimator is consistent, we can make the probability as close to 1.0 or as small as we like by drawing a sufficiently large sample.  Efficiency: An estimator “A” is said to be more efficient than an estimator “B” when “A” has a smaller sampling variance, i.e. if the specific values of “A” are more tightly clustered around their expectation. Standard error of the Regression (SER): It is defined as estimating the standard deviation of the error term in a regression model. Standard error of regression coefficient: It is defined as estimating the standard deviation of the sampling distribution for a particular coefficient term. P-value: P-value is the probability when the null hypothesis is considered true, of drawing sample data that are as adverse to the null as the data drawn, or more so. When the p-value is small, there are two possibilities for that – firstly, a low-probability unrepresentative sample is drawn, or secondly, the null hypothesis is false. Significance level: For a hypothesis test, the significance test is the smallest p-value for which the null hypothesis is not rejected. If the significance level is 1%, the null is rejected if and only if the p-value for the test is less than 0.01. The significance level can also be defined as the probability of making a type 1 error, i.e. rejecting a true null hypothesis. Multicollinearity: It is a situation where there is a high degree of correlation among the independent variables in a regression mod. In other words, a situation where some of the X values are close to being linear combinations of other X values. Multicollinearity occurs due to large standard errors and when the regression model cannot produce precise parameter estimates. This problem mainly occurs while estimating causal influences.T-test: The t-test is a common test for the null hypothesis that Bi's particular regression parameter has some specific value. F-test: F-test is a method for jointly testing a set of linear restrictions on a regression model. Omitted variable bias: Omitted variable bias is a bias in estimating regression parameters. It generally occurs when a relevant independent variable is omitted from a model, and the omitted variable is correlated with one or more of the included variables. Log variables: It is a transformation method that allows the estimation of a non-linear model using the OLS method to exchange the natural log of a variable for the level of that variable. It is performed for the dependent variable and/or one or more independent variables. Quadratic terms: This is another common transformation method where both xi and x2i are included as regressors. The estimated effect of xi on y is calculated by finding the derivative of the regression equation concerning xi.  Interaction terms: These are the pairwise products of the "original" independent variables. The interaction terms allow for the possibility that the degree to which xi affects y depends on the value of some other variable Xj. For example, the effect of experience on wages xi might depend on the gender xj of the worker. What are the tips to avoid common problems working with regression analysis? Regression is a very powerful statistical analysis that offers high flexibility but presents a variety of potential pitfalls. Let us see some tips to overcome the most common problems whilst working with regression analysis.Tip 1:  Research Before Starting Before you start working with regression analysis, review the literature to understand the relevant variables, the relationships they have, and the expected coefficient signs and effect magnitudes. It will help you collect the correct data and allow you to implement the best regression equation.  Tip 2: Always prefer Simple Models Start with a simple model and then make it more complicated only when needed. When you have several models with different predictive abilities, always prefer the simplest model because it will be more likely to be the best model. Another significant benefit of simpler models is that they are easier to understand and explain to others.  Tip 3: Correlation Does Not Imply Causation  Always remember correlation doesn't imply causation. Causation is a completely different thing as compared to causation. In general, to establish causation, you need to perform a designed experiment with randomization. However, If you’re using regression analysis to analyze the uncollected data in an experiment, causation is uncertain.Tip 4: Include Graphs, Confidence, and Prediction Intervals in the Results   The presentation of your results can influence the way people interpret them. For instance, confidence intervals and statistical significance provide consistent information.  According to a study, statistical reports that refer only to statistical significance only bring about correct interpretations 40% of the time. On the other hand, when the results also include confidence intervals, the percentage rises to 95%. Tip 5: Check the Residual Plots Residual plots are the quickest and easiest method to examine the problems in a regression model and allow you to make adjustments. For instance, residual plots help display patterns when you cannot model curvature present in your data. Regression Analysis and The Real World  Let us summarize what we have covered in this article so far: Regression Analysis and its importance. Difference between regression and classification. Regression Line and Regression Equation. How companies use regression analysis When to use regression analysis. Assumptions in Regression Analysis. Simple and Multiple linear regression. R-squared: Representation, Interpretation, Calculation. Types of Regression. Terminologies used in Regression. How to avoid problems in regression. Regression Analysis is an interesting machine learning technique utilized extensively by enterprises to transform data into useful information. It continues to be a significant asset to many leading sectors starting from finance, education, banking, retail, medicine, media, etc.   Blogs 5627 Regression Analysis And Its Techniques in Data Sci... by Priyankur Sarkar 07 Jan 2022 As a Data Science enthusiast, you might already ... Read More Top Job Roles With Their Salary Data in the World of Data Science for 2020–2022. By Priyankur Sarkar . Data Science requires the expertise of professionals who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. Data science is woven into the fabric of our daily lives in myriad ways that we may not even be aware of; starting from the online purchases we make, our social media feeds, the music we listen to or even the movie recommendations that we are shown online.  For several years in a row, the job of a data scientist has been hailed as the “hottest job of the 21st century”. Data scientists are among the highest paid resources in the IT industry. According to Glassdoor, the average data scientist’s salary is $113,436. With the growth of data, the demand for data science job roles in companies has been rising at an accelerated pace.  How Data Science is a powerful career choice The landscape of a data science job is promising and full of opportunities spanning different industries. The nature of the job allows an individual to take on flexible remote jobs and also to be self-employed.  The field of data science has grown exponentially in a very short time, as companies have come to realize the importance of gathering huge volumes of data from websites, devices, social media platforms and other sources, and using them for business benefits. Once the data is made available, data scientists use their analytical skills, evaluate data and extract valuable information that allows organizations to enhance their innovations. A data scientist is responsible for collecting, cleansing, modifying and analyzing data into meaningful insights. In the first phase of their career, a data scientist generally works as a statistician or data analyst. Over many years of experience, they evolve to be data scientists.  The ambit of data has been increasing rapidly which has urged companies to actively recruit data scientists to harness and leverage insights from the huge quantities of valuable data available, enabling efficiency in processes and operations and driving sales and growth.  In the future, data may also emerge as the turning point of the world economy. So, pursuing a career in data science would be very useful for a computer enthusiast, not only because it pays well but also since it is the new trend in IT. According to the Bureau of Labor Statistics (BLS), jobs for computer and information research scientists, as well as data scientists are expected to grow by 15 percent by the year 2028. Who is a Data Scientist & What Do They Do? Data Scientists are people with integral analytical data expertise together with complex problem-solving skills, besides the curiosity to explore a wide range of emerging issues.  They are considered to be the best of both the sectors – IT and business, which makes them extremely skilled individuals whose job roles straddle the worlds of computer science, statistics, and trend analysis. Because of this surging demand for data identification and analysis in various tech fields like AI, Machine Learning, and Data Science, the salary of a data scientist is one of the highest in the world. Requisite skills for a data scientist Before we see the different types of jobs in the data analytics field, we must be aware of the prerequisite skills that make up the foundation of a data scientist: Understanding of data – As the name suggests, Data Science is all about data. You need to understand the language of data and the most important question you must ask yourself is whether you love working with data and crunching numbers. And if your answer is “yes”, then you’re on the right track. Understanding of algorithms or logic – Algorithms are a set of instructions that are given to a computer to perform a particular task. All Machine Learning models are based on algorithms, so it is quite an essential prerequisite for a would-be data scientist to understand the logic behind it.  Understanding of programming – To be an expert in data science, you do not need to be an expert coder. However, you should have the foundational programming knowledge which includes variables, constants, data types, conditional statements, IO functions, client/server, Database, API, hosting, etc. If you feel comfortable working with these and you have your coding skills sorted, then you’re good to go. Understanding of Statistics – Statistics is one of the most significant areas in the field of Data Science. You should be well aware of terminologies such as mean, median, mode, standard deviation, distribution, probability, Bayes’ theorem, and different Statistical tests like hypothesis testing, chi-square, ANOVA, etc. Understanding of Business domain – If you do not have an in-depth working knowledge of the business domain, it will not really prove to be an obstacle in your journey of being a data scientist. However, if you have the primitive understanding of the specific business area you are working for, it will be an added advantage that can take you ahead. Apart from all the above factors, you need to have good communication skills which will help the entire team to get on the same page and work well together.Data Science Job Roles  Data science experts are in demand in almost every job sector, and are not confined to the IT industry alone.  Let us look at some major job roles, associated responsibilities , and the salary range: 1. Data ScientistsA Data Scientist’s job is as exciting as it is rewarding. With the help of Machine Learning, they handle raw data and analyze it with various algorithms such as regression, clustering, classification, and so on. They are able to arrive at insights that are essential for predicting and addressing complex business problems.  Responsibilities of Data Scientists The responsibilities of Data Scientists are outlined below: Collecting huge amounts of organized and unorganized data and converting them into useful insights. Using analytical skills like text analytics, machine learning, and deep learning to identify potential solutions which will help in the growth of organizations. Following a data-driven approach to solve complex problems.  Enhancing data accuracy and efficiency by cleansing and validating data. Using data visualization to communicate significant observations to the organization’s stakeholders. Data Scientists Salary Range According to Glassdoor, the average Data Scientist salary is $113,436 per annum. The median salary of an entry-level professional can be around $95,000 per annum. However, early level data scientists with 1 to 4 years' experience can get around $128,750 per annum while the median salary for those with more experience ranging around 5 to 9 years  can rise to an average of $165,000 per annum. 2. Data Engineers  A Data Engineer is the one who is responsible for building a specific software infrastructure for data scientists to work. They need to have in-depth knowledge of technologies like Hadoop and Big Data such as MapReduce, Hive, and SQL. Half of the work of Data Engineers is Data Wrangling and it is advantageous if they have a software engineering background. Responsibilities of Data Engineers  The responsibilities of Data Engineers are described below: Collecting data from different sources and then consolidating and cleansing it. Developing essential software for extracting, transforming, and loading data using SQL, AWS, and Big Data. Building data pipelines using machine learning algorithms and statistical techniques. Developing innovative ways to enhance data efficiency and quality. Developing, testing and maintaining data architecture. Required Skills for Data Engineers  There are certain skill sets that data engineers need to have: Strong skills in analytics to manage and work with massive unorganized datasets. Powerful programming skills in trending languages like Python, Java, C++, Ruby, etc. Strong knowledge of database software like SQL and experience in relational databases. Managerial and organizational skills along with fluency in various databases.  Data Engineers’ Salary Range According to Glassdoor, the average salary of a Data Engineer is $102,864 in the USA. Reputed companies like Amazon, Airbnb, Spotify, Netflix, IBM value and pay high salaries to data engineers. Entry-level data and mid-range data engineers get an average salary between $110,000 and $137,770 per annum. However, with experience, a data engineer can get up to $155,000 in a year. 3. Data Analyst As the name suggests, the job of a Data Analyst is to analyze data. A data analyst collects, processes, and executes statistical data analyses which help business users to develop meaningful insights. This process requires creating systems using programming languages like Python, R or SAS. Companies ranging from IT, healthcare, automobile, finance, insurance employ Data Analysts to run their businesses efficiently.  Responsibilities of Data Analysts  The responsibilities of Data Analysts are described below: Identifying correlations and gathering valuable patterns through data mining and analyzing data. Working with customer-centric algorithms and modifying them to suit individual customer demands. Solving certain business problems by mapping data from numerous sources and tracing them. Creating customized models for customer-centric market strategies, customer tastes, and preferences. Conducting consumer data research and analytics by deploying statistical analysis. Data Analyst Salary Range According to Glassdoor, the national average salary of a Data Analyst is $62,453 in the United States. The salaries of an entry-level data analyst start at  $34,5000 per year or $2875 per month.  Glassdoor states that a junior data analyst earns around $70,000 per year and experienced senior data analysts can expect to be paid around $107,000 per year which is roughly $8916 per month. Key Reasons to Become a Data Scientist Becoming a Data Scientist is a dream for many data enthusiasts. There are some basic reasons for this: 1. Highly in-demand field The job of Data Science is hailed as one of the most sought after jobs for 2020 and according to an estimate, it is predicted that this field would generate around 11.5 million jobs by the year 2026. The demand for expertise in data science is increasing while the supply is too low.  This shortage of qualified data scientists has escalated their demand in the market. A survey by the MIT Sloan Management Review indicates that 43 percent of companies report that a major challenge to their growth has been a lack of data analytic skills. 2. Highly Paid & Diverse Roles Since data analytics form the central part of decision-making, companies are willing to hire larger numbers of data scientists who can help them to make the right decisions that will boost business growth. Since it is a less saturated area with a mid-level supply of talents, various opportunities have emerged that require diverse skill sets. According to Glassdoor, in the year 2016,  data science was the highest-paid field across industries. 3. Evolving workplace environments With the arrival of technologies like Artificial Intelligence and Robotics which fall under the umbrella of data science, a vast majority of manual tasks have been replaced with automation.  Machine Learning has made it possible to train machines to perform repetitive tasks , freeing up humans to focus on critical problems that need their attention. Many new and exciting technologies have emerged within this field such as Blockchain, Edge Computing, Serverless Computing, and others.  4. Improving product standards The rigorous use of Machine Learning algorithms for regression, classification recommendation problems like decision trees, random forest, neural networks, naive Bayes etc has boosted the customer experiences that companies desire to have. One of the best examples of such development is the E-commerce sites that use intelligent Recommendation Systems to refer products and provide customer-centric insights depending upon their past purchases. Data Scientists serve as a trusted adviser to such companies by identifying the preferred target audience and handling marketing strategies. 5. Helping the world In today’s world, almost everything revolves around data. Data Scientists extract hidden information from massive lumps of data which helps in decision making across industries ranging from finance and healthcare to manufacturing, pharma, and engineering. Organizations are equipped with data-driven insights that boost productivity and enhance growth, even as they optimize resources and mitigate potential risks. Data Science catalyzes innovation and research, bringing positive changes across the world we live in. Factors Affecting a Data Scientist’s Salary The salaries of Data Scientists can depend upon several factors. Let us study them one by one and understand their significance: Data Scientist Salary by Location The number of job opportunities and the national data scientist salary for data innovators is the highest in Switzerland in the year 2020, followed by the Netherlands and the United Kingdom. However, since Silicon Valley in the United States is the hub of new technological innovations, it is considered to generate the most jobs for startups in the world, followed by Bangalore in India. A data scientist’s salary in Silicon Valley or Bangalore is likely to be higher than in other countries. Below are the highest paying countries for data scientist roles along with their average annual data science salary: Switzerland$115,475Netherlands$68,880Germany$64,024United Kingdom$59,781Spain$30,050Italy$37,785Data Scientist Salary by ExperienceA career in the field of data science is very appealing to young IT professionals. Starting salaries are very lucrative, and there is incremental growth in salary  with  experience. Salaries of a data scientist depend on the expertise, as well as the years of experience: Entry-level data scientist salary – The median entry-level salary for a data scientist is around $95,000 per year which is quite high. Mid-level data scientist salary –   The median salary for a mid-level data scientist having experience of around 1 - 4 years is $128,750 per year. If the data scientist is in a managerial position, the average salary rises upto $185,000 per year. Experienced data scientist salary –  The median salary for an experienced data scientist having experience of around 5 - 9 years is $128,750 per year whereas the median salary of an experienced manager is much higher; around $250,000 per year. Data Scientist Salary by Skills There are some core competencies that will help you to shine in your career as a Data Scientist, and if you want to get the edge over your peers you should consider polishing up these skills: Python is the most crucial and coveted skill which data scientists must be familiar with, followed by R. The average salary in the US for  Python programmers is $120,365 per annum. If you are well versed in both Data Science and Big Data, instead of just one among them, your salary is likely to increase by at least 25 percent . The users of innovative technology like the Statistical Analytical System get a salary of around $77,842. On the other hand, users of software analysis software like SPSS have a pay scale of  $61,452 per year. Machine Learning Engineers on the average earn around $111,855 per year. However, with more experience in Machine Learning along with knowledge in Python, you can earn around $146,085 per annum. A Data Scientist with domain knowledge of Artificial Intelligence can earn an annual salary between $100,000 to $150,000. Extra skills in programming and innovative technologies have always been a value-add that can enhance your employability. Pick skills that are in-demand to see your career graph soar. Data Scientist Salary by Companies Some of the highest paying companies in the field of Data Science are tech giants like Facebook, Amazon, Apple, and service companies like McGuireWoods, Netflix or Airbnb.  Below is a list of top companies with the highest paying salaries: McGuireWoods$165,114Amazon$164,114Airbnb$154,879Netflix$147,617 Apple$144,490Twitter$144,341Walmart$144,198Facebook$143,189eBay$143,005Salaries of Other Related Roles Various other job roles associated with Data Science are also equally exciting and rewarding. Let us look at some of them and their salaries: Machine Learning Engineer$114,826Machine Learning Scientist$114,121Applications Architect$113,757Enterprise Architect$110,663Data Architect$108,278Infrastructure Architect$107,309Business Intelligence Developer$81,514Statistician$76,884ConclusionLet us look at what we have learned in this article so far: What is Data Science? The job of a Data Scientist Pre-requisite skills for a Data Scientist Different job roles Key reasons for becoming a Data Scientist Salary depending upon different factors Salary of other related roles The field of Data Science is ripe in terms of opportunities for Data Scientists, Data Engineers, and Data Analysts. The figures mentioned in this article are not set in stone and may vary depending upon the skills you possess, experience you have and various other factors. With more experience and skills, your salary is bound to increase by a certain percentage every year. Data science is a field that will revolutionize the world in the coming years and you can have a share of this very lucrative pie with the right education qualifications, skills, experience and training.   Blogs 6953 Top Job Roles With Their Salary Data in the World ... by Priyankur Sarkar 12 Sep 2020 14 mins read Data Science requires the expertise of professio... Read More How To Become A Data Aanalyst In 2022? By Dipayan Ghatak . In 2022, Data Analysis has become one of the core functions in any organization. This is a highly sought-after role that has evolved immensely in the past few years. But what is Data Analysis?  What do Data Analysts do? How to become a Data Analyst in 2022? What are the skills one needs to have to be a Data Analyst? There are many such questions which strike our mind when we talk about this profession.Let's walk through the answers to all the questions to ensure we have a clear picture in our mind.What is Data analytics?Information collected from different sources is used to make informed decisions for the organization, and is analyzed for some specific goals through Data Analysis. Data Analysis is not only used for research and analysis; but it helps organizations learn more about their customers, develop marketing strategies and optimize product development, just to name a few areas where it makes an impact.To be precise, there are four types of Data Analytics:Descriptive Analytics: - In this type of analytics, analysts examine the past data like monthly sales, monthly revenue, website traffic and more to find out the trend. They then draft a description or a summary of the performance of the firm or website. This type of analytics uses arithmetic operations, mean, median, max, percentage and other statistical summaries.Diagnostic Analytics:- As the name suggests, here we diagnose the data and find out the reasons behind any particular trend, issue or scenario.  If a company is faced with any negative data then this type of analysis will help them to find out the main reasons/causes for the decline in the performance, against which decisions and actions can be taken.Predictive Analytics:-  This type of analytics helps in predicting the future outcome by analyzing the past data and trends. It will help companies to take proactive actions for the better outcomes. Not just this, but predictive analysis also helps us forecast the sales, demand, fraud, failures and set our budgets and other resources accordingly.  Prescriptive Analytics:- This type of analytics helps in determining what action the company should take next in response to the situation, to keep the business going and growing.Why do we need Data Analysts?Organizations across different fields or sectors rely on data analysis to take important decisions for the development of a new product, to forecast sales for the near future, or find out about entry into new markets or new customers to target. Data analysis is also used to analyze the business performance based on the present data and find out various inefficiencies in the organizations. Not only industries or businesses use data analysis, but it is also used by different political parties and other groups to find out about opportunities as well as challenges.What does a data analyst do?There are several functions which an analyst performs, but some of the functions may also depends on the type of business and organization. Generally, a data analyst performs the following responsibilities:Data collection from various sources like primary sources and secondary sources and arranging the data in a proper sequence.Cleaning and processing the data as per requirement. A data analyst may be required to treat missing values, clean invalid or wrong data and remove unwanted information.Using different kind of statistical tools like R, Python, SPSS or SAS, to interpret the data collected.Adjusting the data according to the upcoming trends or changes like seasonal trends and then making interpretations.Preparing a data analysis report.Identifying opportunities and threats from the analyzed data and apprising the same to the organization.Now that you know what areas a Data Analyst works on, let us move to the skills and knowledge you would require to get started in this field.What are the skills necessary to be a Data Analyst?Broadly, a data analyst needs to have two type of skills at a broader level:Technical skills - Knowledge of different technical languages and tools like R, SQL, Microsoft Excel, Tableau, Mathematical skills, statistical skills and data visualization skills. These technical skills would help an analyst actually use the data and visualize the final outcome in a form that could be beneficial for the firm. This may include tables, graphs, charts, and more.   Decision making – This is extremely necessary to present the outcome and take the executives through the various changes, trends, demand, and downfall. Deep analysis is required to be able to take logical, factual and beneficial decisions for the firm. Data analysts must have the ability to think strategically and get a 360 degree view of the situation, before suggesting the way forward.After acquiring the above mentioned skills, it is very much required to keep yourself updated with the latest trends in the industry, so a mindset of continuous learning is a must.How to become a data analyst in 2022?The year 2022 changed all the definitions of a business and its processes. COVID-19 put companies across the world in a tailspin, forcing them to rethink their business strategies in order to cope with the evolving challenges thrown up by the pandemic. Some companies that were market leaders in their domain were unable to cope, and many had to even close down. The question therefore arises, in such an uncertain scenario, with challenges around every corner, is it even prudent to consider stepping into the role of a Data Analyst at this juncture?   The answer to this is, “YES”. This is the best time to be a data analyst because organizations everywhere are looking for expert Analysts who can guide them in making the right decisions, helping the organization to survive through the pandemic and beyond. Data analysts can perform detailed sales forecasting, or carry out a complete market analysis to make the right predictions for future growth. Companies need to prepare smart strategies for sales and marketing, to survive and thrive in the long run.If you want to shape your career in data analytics then You must have a degree in Mathematics, Economics, Engineering, Statistics or in a field which emphasizes on statistical and analytical skills. You must know some data analytics tools or skills which are mentioned above like R, SQL, Tableau, Data Warehousing, Data visualization, Data mining and advanced Microsoft Excel. You must consider some good certifications in the above-mentioned skills.   You may also consider a master’s degree in the field of data analytics.Let us now take you through the scope of Data Analysis in 2022.What is the scope of data analytics in 2022?The world is witnessing a surge in demand for data analytics services. According to a report, it is expected that there will be 250,000 new openings in the Data Analytics field in 2022, which is almost 60% higher than the demand in 2019-20. To stay ahead of the competition, organizations are employing Data Analysts and the demand for experts in the field is only set to rise. According to another report published in 2019, there were 150000 jobs which were vacant in the Data Analytics sector because of lack of available talent. This is a lucrative field, and those professionals who have expertise and experience can easily climb to the top in a short time. A report by IBM predicts that by 2022, Data Science and Analytics jobs would grow to nearly 350,000.What are the sectors in which Data Science jobs are expected to grow in India in 2022?Though the need for data analytics is growing across every sector, there are a few sectors that are more in-demand than others. These include:Aviation sector: uses data analysis for pricing and route optimization.Agriculture sector: analyses data to forecast the output and pricing.Cyber security: Global companies are adopting data engineering and data analysis for anomaly and intrusion detection.Genomics: Data analytics is used to study the sequence of genomes. It is heavily used to diagnose abnormalities and identify diseases.Conclusion If you would like to enter the field of Data Analytics, there’s no time like now! Data is useless without the right professional to analyze it. Leading companies leverage the power of analytics to improve their decision making and fuel business growth, and are always looking to employ bright and talented professionals with the capabilities they need.  Opportunities are plentiful and the rewards are immense, so take the first step and start honing all the skills that can make you fulfil your dream! Blogs 8925 How To Become A Data Aanalyst In 2022? by Dipayan Ghatak 20 Feb 2021 8 mins read In 2022, Data Analysis has become one of the core ... Read More Load More Machine learning Series. What is Machine Learning and Why It Matters: Everything You Need to Know Machine Learning Algorithms: [With Essentials, Principles, Types & Examples covered] Overfitting and Underfitting With Algorithms in Machine Learning What is Bias-Variance Tradeoff in Machine Learning What is Gradient Descent For Machine Learning What is Linear Regression in Machine Learning What is Logistic Regression in Machine Learning What is LDA: Linear Discriminant Analysis for Machine Learning What is Naive Bayes in Machine Learning What is K-Nearest Neighbor in Machine Learning: K-NN Algorithm Support Vector Machines in Machine Learning What are Decision Trees in Machine Learning (Classification And Regression) Bagging and Random Forest in Machine Learning Boosting and AdaBoost in Machine Learning Top 30 Machine Learning Skills required to get a Machine Learning Job Useful links. Big Data Analytics training online in Dammam Professional Scrum Developer Psd classes in Newark Kanban Management Professional training in Berlin SAFe 4.0 Scrum Master With SSM course in Tampa Kubernetes course in Mexico SAFe 4.6 Product Manager Product Owner (SPMPO) classes in Kansas City MongoDB interview questions and answers Different levels of CMM Leading Safe classroom training in Riyadh Xamarin training in Perth Subscribe to our newsletter. Thank You! We have sent an e-mail with a link to confirm your subscription. × Subscribe to our newsletter. . Login Forgot Password Register Log In With Facebook Log In With Google Login with linkedin We do not post without your permission. Subscribe to Newsletter We do not post without your permission. ×
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                            [body] => 5 Skills You Need to Become a Machine Learning Engineer April 7, 2016 | 6 min read Arpan Chakraborty Back 5 Skills You Need to Become a Machine Learning Engineer Share This blog post has been updated on July 2, 2021. Are you interested in machine learning? You’re not alone! More people are getting interested in machine learning every day. In fact, you’d be hard pressed to find a field generating more buzz these days than this one.   Machine learning’s inroads into our collective consciousness have captured our imaginations, since AlphaGo won four out of five Go matches against the world’s best Go player in 2016.  There has been no looking back for machine learning technology and the opportunities it offers since then. It is the third most in-demand artificial intelligence job in the United States currently with a median salary of $125,000, according to Indeed.com. If you’re interested in learning machine learning skills to enter this field, your moment is now.  That said, it’s one thing to get interested in machine learning, it’s another thing altogether to actually start working in the field. This post will help you understand both the overall mindset and the specific skills you’ll need to start working as a machine learning engineer. What is a Machine Learning Engineer? To begin, there are two very important things that you should understand if you’re considering a career as a machine learning engineer. First, it’s not a “pure” academic role. You don’t necessarily have to have a research or academic background. Second, it’s not enough to have either software engineering or data science experience. You ideally need both. It’s also critical to understand the differences between a data analyst, data scientist and a machine learning engineer. In simplest form, the key distinction has to do with the end goal.  As a data analyst, you’re analyzing data in order to tell a story, and to produce actionable insights for members of your team. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what’s been presented. The “audience” for your output is human.  As a machine learning engineer, on the other hand, your final “output” is working software (not the analyses or visualizations that you may have to create along the way), and your “audience” for this output often consists of other software components that run autonomously with minimal human supervision.  Intelligence is still meant to be actionable, but in the machine learning model, the decisions are being made by machines and they affect how a product or service behaves. This is why the software engineering skill set is so important to a career in Machine Learning.  A data scientist lives somewhere between these two worlds. They must have the software engineering skills to collect, clean, and organize data to analyze, and use machine learning to extract insights. Their communication skills are also vital to their success.  How to Be a Successful Machine Learning Engineer: Understand the Ecosystem. Before getting into specific skills, there is one more concept to address. Being a machine learning engineer necessitates understanding the entire ecosystem that you’re designing for. Let’s say you’re working for a grocery chain, and the company wants to start issuing targeted coupons based on things like the past purchase history of customers, with a goal of generating coupons that shoppers will actually use.  In a data analysis model, you could collect the purchase data, do the analysis to figure out trends, and then propose strategies. The machine learning approach would be to write an automated coupon generation system. But what does it take to write that system, and have it work? You have to understand the whole ecosystem—inventory, catalog, pricing, purchase orders, bill generation, Point of Sale software, CRM software, etc. Ultimately, the process is less about understanding machine learning algorithms—or when and how to apply them—and more about understanding the systemic interrelationships, and writing working software that will successfully integrate and interface. Remember, machine learning output is actually working software! What It Takes to Become a Machine Learning Engineer. Now, let’s get into the real details of what it takes to be a machine learning engineer. We’re going to break this into two primary sections:  Skills, and  Languages and libraries We’ll begin with the skills here, then in a follow up post we’ll address languages and libraries for machine learning. 1. Computer Science Fundamentals and Programming. Computer science fundamentals important for machine learning engineers include:  Data structures – stacks, queues, multi-dimensional arrays, trees, graphs, etc. Algorithms – searching, sorting, optimization, dynamic programming, etc. Computability and complexity – P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.  Computer architecture – memory, cache, bandwidth, deadlocks, distributed processing, etc. You must be able to apply, implement, adapt or address them (as appropriate) when programming. Practice problems, coding competitions and hackathons are a great way to hone your skills. 2. Probability and Statistics. A formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.) are at the heart of many machine learning algorithms; these are a means to deal with uncertainty in the real world.  Closely related to this is the field of statistics, which provides various measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis testing, etc.) that are necessary for building and validating models from observed data. Many machine learning algorithms are essentially extensions of statistical modeling procedures. 3. Data Modeling and Evaluation. Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, eigenvectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.).  A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.).  Iterative learning algorithms often directly utilize resulting errors to tweak the model (e.g. backpropagation for neural networks), so understanding these measures is very important even for just applying standard algorithms. 4. Applying Machine Learning Algorithms and Libraries. Standard implementations of machine learning algorithms are widely available through libraries/packages/APIs (e.g. scikit-learn, Theano, Spark MLlib, H2O, TensorFlow etc.), but applying them effectively involves choosing a suitable model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc.), a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning.  You also need to be aware of the relative advantages and disadvantages of different approaches, and the numerous gotchas that can trip you (bias and variance, overfitting and underfitting, missing data, data leakage, etc.). Data science and machine learning challenges such as those on Kaggle are a great way to get exposed to different kinds of problems and their nuances. 5. Software Engineering and System Design. At the end of the day, a machine learning engineer’s typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services.  You need to understand how these different pieces work together, communicate with them (using library calls, REST APIs, database queries, etc.) and build appropriate interfaces for your component that others will depend on. Careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data.  Software engineering best practices (including requirements analysis, system design, modularity, version control, testing, documentation, etc.) are invaluable for productivity, collaboration, quality and maintainability. Machine Learning Job Roles. Jobs related to machine learning are growing rapidly as companies try to get the most out of emerging technologies. The chart below depicts the relative importance of core skills for these general types of roles, with a typical data analyst role for comparison. Relative importance of core skills for different Machine Learning job roles (click to enlarge) Get Ready for the Future of Machine Learning. What is perhaps most compelling about machine learning is its seemingly limitless applicability. There are already so many fields being impacted by Machine Learning, including education, finance, computer science, and more.  In some cases, machine learning techniques are desperately needed. Healthcare is an obvious example. Machine Learning techniques are already being applied to critical arenas within the healthcare sphere, impacting everything from care variation reduction efforts to medical scan analysis.  David Sontag, an assistant professor at New York University’s Courant Institute of Mathematical Sciences and NYU’s Center for data science, gave a talk on machine learning and the healthcare system, in which he discussed “how machine learning has the potential to change health care across the industry, from enabling the next-generation electronic health record to population-level risk stratification from health insurance claims.” The world is unquestionably changing in rapid and dramatic ways, and the demand for machine learning engineers is going to keep increasing exponentially. The world’s challenges are complex, and they will require complex systems to solve them.  Machine learning engineers are building these systems. If this is your future, then there’s no time like the present to start mastering the skills and developing the mindset you’re going to need to succeed. Enroll Now in our Machine Learning Engineer Nanodegree program. Start Learning Arpan Chakraborty Arpan likes to find computing solutions to everyday problems. He is interested in human-computer interaction, robotics and cognitive science. He obtained his PhD from North Carolina State University, focusing on biologically-inspired computer vision. At Udacity, he develops content for artificial intelligence and machine learning courses. View All Posts by Arpan Chakraborty Popular Nanodegrees Programming for Data Science with Python Data Scientist Nanodegree Self-Driving Car Engineer Data Analyst Nanodegree Android Basics Nanodegree Intro to Programming Nanodegree AI for Trading Predictive Analytics for Business Nanodegree AI For Business Leaders Data Structures & Algorithms School of Artificial Intelligence School of Cyber Security School of Data Science School of Business School of Autonomous Systems School of Executive Leadership School of Programming and Development Related Articles Moving Files in Python. Most programs today require the manipulation of files and folders at some point. As a programmer, you... Moving Files in Python, Programming Languages, Python Multiple Inheritance in C++. A C++ programmer strives for readable and reusable code. Easy-to-read code saves time and effort when making... C++, C++ inheritance, Programming Languages Python Ordered Sets: An Overview. Programming is mainly about handling data. As a Python developer, you’ll find ways to store data in... Programming Languages, Python, Python Ordered Sets 5 Emerging American Tech Cities in 2021. When you think of the tech industry, your first thought might be San Francisco, Silicon Valley, or... tech cities [et_bloom_locked optin_id=”optin_4″] Click below to download your preferred Career Guide. Web Developer Career Guide Cloud Career Guide Data Career Guide Robotics Career Guide [/et_bloom_locked] ×
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                            [title] => Must Have Machine Learning Engineer Skills | Become an ML Engineer | Edureka
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                            [description] => This article will provide you with the crisp knowledge of the Technical as well as Non-Technical Machine Learning Engineer Skills
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                            [body] => Top 10 Skills to Become a Machine Learning Engineer Last updated on Dec 16,2021 10.1K Views Kislay Keshari Kurt is a Big Data and Data Science Expert, working as a... Kurt is a Big Data and Data Science Expert, working as a Research Analyst at Edureka. He is keen to work with Machine Learning,... Bookmark Become a Certified ProfessionalWe are living in the world of humans and machines. Humans have been evolving and learning from their past experience for millions of years. On the other hand, the era of machines and robots have just begun. The future of machine is enormous and is beyond our scope of imagination. We leave this great responsibility on the shoulder of a particular individual namely Machine Learning Engineer. So, let us have a look at some of Machine Learning Engineer Skills required to become a successful ML Engineer.In this article I’ll be covering the following Topics: Who is an ML Engineer? Technical Skills Non-Technical Skills Bonus Skills So before we get acquainted with the Machine Learning Engineer Skills, Let us first understand who exactly is a ML Engineer. Who is a Machine Learning Engineer?Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.Artificial intelligence is the goal of a machine learning engineer. They are computer programmers, but their focus goes beyond specifically programming machines to perform specific tasks. They create programs that will enable machines to take actions without being specifically directed to perform those tasks.Transform yourself into a highly skilled professional and land a high-paying job with the Artificial Intelligence Course.Now that we know, who is an ML Engineer, let’s move forward to the Machine Learning Engineer Skills one by one.Machine Learning Engineer Skills. Programming Languages(R/Java/Python/C++).  The first and foremost requirement is to have a good grip on a programming language, preferably python as it is easy to learn and its applications are wider than any other language. Python is the lingua franca of Machine Learning.It is important to have a good understanding of topics like Data structures, Memory management, and classes. Although Python is a very good Language, it alone cannot help you. You will probably have to learn all these languages like C++, R, Python, Java and also work on MapReduce at some point. Statistics.  Familiarity with Matrices, Vectors and Matrix Multiplication is required. A good understanding of Derivatives and Integrals is necessary, cause even simple concepts like gradient descent might elude you.Statistical concepts like Mean, Standard Deviations and Gaussian Distributions are required along with probability theory for algorithms like Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. Signal Processing Techniques. One of the few Machine Learning Engineer Skills is also the understanding of Signal Processing and having the ability to solve different problems using Signal Processing techniques as feature extraction is one of the most important parts of Machine Learning.Knowledge of Time-frequency Analysis and Advanced Signal Processing Algorithms such as   Wavelets, Shearlets, Curvelets, and Bandlets will help you to solve complex situations. Applied Mathematics. A lot of machine learning techniques out there are just fancy types of function approximation. Having a firm understanding of Algorithm theory and understanding subjects such as Gradient Descent, Convex Optimizations, Quadratic Programming, and Partial Differentiation will help a lot. Neural Network Architectures.  Neural Networks are a class of models within the general machine learning literature. Neural networks are a specific set of algorithms that have revolutionized machine learning.We need machine learning for tasks that are too complex for humans to code directly, i.e. tasks that are so complex that it is impractical. Neural Networks are themselves general function approximations, which is why they can be applied to almost any machine learning problem about learning a complex mapping from the input to the output space.Neural networks have been by far the most accurate way of approaching many problems, like translation, speech recognition, and image classification. Language, Audio and Video Processing. Since Natural Language Processing combines two of the major areas of work ie. Linguistics and Computer Science and chances are at some point you’re going to work with either text or audio or video. So it’s necessary to have good control over libraries like Gensim, NLTK, and techniques like word2vec, sentimental analysis, and summarization.Voice and Audio analysis involves extracting useful information from the audio signals themselves. Being well versed in math and concepts of Fourier Transformation will get you far in this one.Now that we have seen the Technical Machine Learning Engineer Skills, let us have a look at the Non-Technical Machine Learning Engineer Skills Industry Knowledge. The most successful machine learning projects out there are going to be those that address real pain points. Whichever industry you’re working for. You should know how that industry works and what will be beneficial for the business.If a Machine Learning Engineer does not have business acumen and the know-how of the elements that make up a successful business model, all those technical skills cannot be channeled productively. You won’t be able to discern the problems and potential challenges that need solving for the business to sustain and grow. You won’t really be able to help your organization explore new business opportunities. Effective Communication. You’ll need to explain ML concepts to people with little to no expertise in the field. Chances are you’ll need to work with a team of engineers, as well as many other teams. Communication is going to make all of this much easier.Companies searching for a strong ML Engineer are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. Rapid Prototyping. Iterating on ideas as quickly as possible is mandatory for finding one that works. In machine learning, this applies to everything from picking the right model, to working on projects such as A/B testing.You need to do a group of techniques used to quickly fabricate a scale model of a physical part or assembly using three-dimensional computer-aided design (CAD) data. Keep Updated. You must stay up to date with any up and coming changes. Every month new neural network models come out that outperform previous architecture.It also means being aware of the news regarding the development of the tools theory and algorithms through research papers, blogs, conference videos, etc. Online community changes quickly. Bonus Machine Learning Engineer Skills. You might be in a situation where you’d like to apply machine learning techniques to systems that will interact with the real world. Having some knowledge of Physics will take you far.Reinforcement Learning has been a driver behind many of the most exciting developments in deep learning and artificial intelligence in 2017. This is will be critical to understanding if you want to go into robotics, Self-driving cars, or any other AI-related area.Computer Vision and Machine Learning are two core branches of Computer Science that can function and power very sophisticated systems that rely on CV and ML algorithms exclusively but when you combine the two, you can achieve even more.So with this, we come to an end of this article. I hope you got an understanding of the various Technical as well as the Non-Technical Machine Learning Engineer Skills that are required to become a successful ML Engineer.Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning, and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.Got a question for us? Please mention it in the comments section of the “Machine Learning Engineer Skills” article and we will get back to youRecommended videos for you. Introduction to Mahout. Watch NowRecommended blogs for you. Deep Learning : Perceptron Learning Algorithm. Read Article PyTorch Tutorial – Implementing Deep Neural Networks Using PyTorch. Read Article 25 Best Free Datasets for Machine Learning. Read Article Supervised Learning In Apache Mahout. Read Article A 101 Guide On The Least Squares Regression Method. Read Article Top 8 Data Science Tools Everyone Should Know. Read Article Understanding Distance Measures in Mahout. Read Article What Are The Prerequisites For Machine Learning? Read Article The Best Machine Learning Libraries For Beginners. Read Article An Overview of Apache Mahout. Read Article AI in Wimbledon: Power Highlights, Analytics and Insights. Read Article Recurrent Neural Networks (RNN) Tutorial | Analyzing Sequential Data Using TensorFlow In Python. Read Article Q Learning: All you need to know about Reinforcement Learning. Read Article What is Cognitive AI? Is It the Future? Read Article Top 10 Data Scientists Myths Regarding Roles in India. Read Article Introduction to Myrrix and Oryx. Read Article Implementing Artificial Intelligence In Healthcare. Read Article Capsule Neural Networks – Set of Nested Neural Layers. Read Article A Step By Step Guide to Install TensorFlow. Read Article Restricted Boltzmann Machine Tutorial – Introduction to Deep Learning Concepts. Read Article Comments 0 Comments Join the discussion Cancel reply. Trending Courses in Artificial Intelligence. 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                            [body] => Skills Required for Machine Learning Rating: 5    15080Get Trained And CertifiedGet Trained And CertifiedShare:search heresearch hereMachine Learning ArticlesTop 10 Machine Learning Algorithms Artificial Intelligence Vs Machine LearningMachine Learning Examples In Real WorldMachine Learning Interview QuestionsTop 10 Simple Machine Learning Projects For BeginnersTop 10 Machine Learning BooksMachine Learning ApplicationsMachine Learning with Python TutorialMachine Learning TechniquesMachine Learning DatasetsSupport Vector Machine Algorithm - Machine LearningTensorFlow Interview QuestionsHow Oracle Embeds Machine-Learning Capabilities Into Oracle DatabaseInstalling TensorFlowMachine Learning with SparkMachine Learning TutorialTensorFlow TutorialTensorFlow Object DetectionTensorFlow 2.0 - A comprehensive platform that supports machine learning workflowsKeras TutorialMachine Learning CommunityExplore real-time issues getting addressed by expertsMachine Learning QuizTest and Explore your knowledge Machine learning is the subfield of Artificial intelligence and it enables computers to learn on their own without being explicitly programmed. Let's get to know how machine learning works. Machine learning refers to an algorithm that is being ingested with a massive amount of data, extracts patterns out of data, and finally converts those patterns into actions. Machine learning started as a very small concept in the beginning, and eventually, it has become part of every industry to embrace new opportunities. Let’s consider a scenario here on how machine learning helps organizations. Have you ever come across the products, appearing in front of your screen or as an advertisement while you are browsing through the internet that is being liked by you or visited by you earlier? Probably you may have!  Get ahead in your career by learning Machine Learning through Mindmajix Machine Learning Online Certification.  You will see or receive product or service suggestions just because of the machine learning algorithms that work behind the scenes. The ultimate goal of these algorithms is to fulfill the final goal of any organization. That's how machine learning induces people to buy the products and services of an organization and helps the organizations in making profits. Your Facebook will show you the news feed that suits your requirement, isn’t it? It is just because of the machine learning algorithms, not only it happens with Facebook but also with all other social media platforms and e-commerce sites. [ Related Article: Top Machine Learning Algorithms ] We have come to know how machine learning application works, and along the way, it is also creating a ton of employment opportunities in the field of IT for data scientists and software engineers. To kick start your career in machine learning or to be a part of this futuristic technology, you need to learn some specific skills Required for Machine Learning, and the pay scale for the machine learning developer is also very high compared to other jobs. [ Related Article: Best Machine Learning Applications ] Here below mentioned are the skills to begin with and advance your career in machine learning. Basic Skills For Machine Learning. Below mentioned are the basic skills one should learn to excel in one career in machine learning technology. Statistics:. Tools and tables are very essential in machine learning to create models from data. Statistics and its branches such as analysis of variance and hypothesis testing are crucial for building algorithms. As machine learning algorithms are built upon statistical models, we can understand how important statistics is for machine learning is. That's how statistics play an essential role in developing the algorithms.  So it is very vital to gain knowledge on statistical tools to speed up your career in machine learning. Probability:. Probability helps in predicting future consequences, and the majority of the algorithms in machine learning work based on uncertain conditions and have to make reliable decisions.  Mathematical equations in probability such as derivative techniques, Bayes Nets, and Markov decisions would help machine learning in predicting the future. Data Modeling:. The critical task of machine learning is to analyze the unstructured data models, which requires the science of data modeling. Data modeling enables identifying the underlying data structures, finding out the patterns, and filling the gaps between the places where data is nonexistent.  Having explicit knowledge of data modeling concepts would help in creating efficient algorithms. [ Related Article: Tutorial on Machine Learning ] Programming Skills:. We are all aware that machine learning mostly depends on algorithms, which means one should possess sound knowledge of different programming languages. Dealing with machine concepts is entirely different from handling small kinds of things such as creating simple applications, scripting web pages, etc. It demands some extra knowledge and skill of programming. Below mentioned are the skills which you require to become a professional in machine learning. Programming Fundamentals and CS: . Machine learning involves computation on large data sets; hence one should possess strong basic fundamental skills such as computer architecture, algorithms, data structures, complexity, etc. Getting in-depth into the programming books and exploring new things will be a good advantage.  Sign Up for some courses to sharpen your programming skills and to advance your knowledge. Applying ML Libraries & Algorithms: . As a machine learning engineer, you need not reinvent the wheel, all you need to do is using the algorithms and libraries that are developed by other developers and organizations. There are a wide variety of API Packages and libraries such as Microsoft’s CNTK, Apache Spark’s MLlib, and Google TensorFlow.  Applying these technologies will require a clear understanding of different concepts, how they can be integrated with different systems, and the pitfalls that arise along the road.  Software Design: . Machine learning should be integrated with various other technologies to leverage its full potential. As a machine learning engineer, you need to develop algorithms and systems that can easily integrate and communicate with the other existing technologies. Hence you are required to have strong skills in Application User Interface (APIs) of various flavors (web APIs, dynamic and static libraries, etc.), and also designing interfaces that would sustain future changes. [ Related Article: Advanced Machine Learning Interview Questions ]   ML Programming Languages. One should certainly require programing knowledge skills to deal with machine learning projects, but the problem is which language one should learn? To clarify this doubt let’s get into the details. Machine learning is not limited by any specific programing language, and it's more like object-oriented language. It would be developed in any language which can meet the required components.  Let’s discuss how each programming language listed below helps in developing machine learning. Python: . Python is one of the widely used programming languages in many fields; in fact, it is mostly preferred by data scientists and machine learning engineers. It is equipped with a wide range of useful libraries which help in processing data efficiently and in scientific computing.  Python also supports a number of machine learning libraries such as Theano, TensorFlow, and sci-kit- learn, and these libraries make training algorithms very easier.  [ Related Article: Python Tutorial Point ] R: . R is a programming language developed for statistical computing and data mining, and these features enable it to be a perfect fit for machine learning tasks. There are many statistical models and algorithms available in R for different computing tasks. When it comes to the syntax of R, it’s a bit different from other languages but is simple to learn. C/C++. C/C++ is generally used to program the interface and to develop the mechanisms of machine learning. The libraries available in all other languages can be firstly developed in C/C++ and can be made available in all other languages with the help of  API calls. C/C++ languages are mostly used to develop embedded systems, and it will help you to a great extent if you are planning to get into the programing of Smart homes, smart cars, and sensor devices.   It becomes difficult for novice programmers when compared to all other programming languages, but it has some great ML libraries in C/C++ such as LibSVM, Shark, and mlpack.  Watch this video on “Top 10 Highest Paying IT Jobs in 2021” and know how to get into these job roles. Other Skills Required. Update yourself:. One should develop strong research skills on topics that are relevant to machine learning. Attending conferences, following news on machine learning, being aware of the changes to different components of machine learning would help you in gaining constant knowledge. Being aware of the changes that are happening in the machine learning field would help you in staying at the top of the game. Read a lot:. Reading will provide you with immense knowledge on the topic, to gain more knowledge on machine learning concepts. There are various free books available on the internet which are loaded with the right amount of information. Read research papers of Google file system, Google Map-Reduce, Google Big Table, etc. Conclusion The technology is evolving to new heights each day, and machine learning has been multiplying in terms of its growth. Organizations across the world are heading towards the implementation of Artificial Intelligence and machine learning in their organizations to scale up. This trend shows how much role machine learning will play in the future of online services and mastering the required skills will place you on a path where opportunities are limitless. Join our newsletterStay updated with our newsletter, packed with Tutorials, Interview Questions, How-to's, Tips & Tricks, Latest Trends & Updates, and more ➤ Straight to your inbox!Course ScheduleNameDatesMachine Learning TrainingJan 15 to Jan 30View DetailsMachine Learning TrainingJan 17 to Feb 01View DetailsMachine Learning TrainingJan 22 to Feb 06View DetailsMachine Learning TrainingJan 24 to Feb 08View DetailsLast updated: 28 Apr 2021About AuthorRavindra SavaramRavindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.read moreRecommended Courses PreviousData Science With Python Training 40 hours 8444.5Deep Learning with Python Training  25 hours 9564.4Data Science Training  30 hours 15964.6Machine Learning with Python Training 30 hours 7114.5Data Science With Python Training 40 hours 8444.5Deep Learning with Python Training  25 hours 9564.4Data Science Training  30 hours 15964.6Machine Learning with Python Training 30 hours 7114.5Data Science With Python Training 40 hours 8444.5Deep Learning with Python Training  25 hours 9564.4 Next1 / 15Explore CoursesCopyright © 2013 - 2022 MindMajix Technologies
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                            [body] => 7 essential skills for Machine Learning and AI developers on AWS By Jorge Vasquez Machine learning and artificial intelligence (ML/AI) are two advanced technologies with the power to transform the way businesses operate and humans interact. ML/AI are already impacting industries like IT, FinTech, healthcare, education and transportation—and it won’t stop there. Companies are becoming more and more laser-focused on AI value, getting out of the experimentation phase and really focusing on accelerating its adoption. This means that software engineers prepared to occupy ML/AI development roles will soon be in higher demand than ever before. These are the seven skills you need to take advantage of the growing opportunity to build great ML/AI solutions: 1. Programming languages . To become an expert in machine learning it’s important to grow your experience with programming languages. According to GitHub, these are the top 10 machine learning languages to take a look at     • Python     • C++     • JavaScript     • Java     • C#     • Julia     • Shell     • R     • TypeScript     • Scala While Python is the most common language among machine learning repositories on GitHub, Scala is becoming increasingly common, especially when it comes to interacting with big data frameworks such as Apache Spark. 2. Data engineering . The first step in machine learning development is pre-processing and storing raw data generated by your systems. For example, let’s imagine an online store that sells a variety of products to customers around the world. This online store will create lots of data related to particular events. When a customer clicks on a product description or purchases a product, new data is generated, and you’ll need to create Extract, Transform and Load (ETL) pipelines that process, clean up and store data so it’s easily accessible for other processes, such as analytics and predictions. For storing data, you could use an object storage such as AWS S3 or a data warehouse such as AWS Redshift. 3. Exploratory data analysis . Being able to perform exploratory data analysis on a dataset is an especially important skill because it allows you to discover interesting patterns in data, identify certain anomalies and test hypotheses. You should be able to: • Create summary statistics for a dataset, for example: • Number of rows and columns • Column data types • Columns that are nullable or not • Column mean, standard deviation, minimum and maximum values, percentile, etc   • Create graphical representations that allow for easy data visualization, for example: • Histograms: for visualizing the distribution of a dataset • Box plots: for a standardized way of displaying the distribution of quantitative data based on a five-number summary: minimum, first quartile, median, third quartile and maximum (You can also identify outliers and see if the distribution of data is symmetrical or skewed.) • Heatmaps: for identifying correlations between variables of a dataset   • Sanitize and prepare data for modelling, for example:  • Remove outliers from your dataset  • Remove correlated variables   • Perform feature engineering to extract more information from your dataset, so you can improve the machine learning model(s) you will build 4. Models . If you want to be a pro in machine learning, you need to be proficient in machine learning algorithms. But that’s not enough; you also need to know when to apply them. For example, if you have a dataset with a series of inputs with their corresponding outputs and you want to find a model that describes the relationship between them, you should use supervised learning algorithms, which can be further grouped into regression (when the output variable is a real value, such as “weight” or “age”) and classification algorithms (when the output variable is a category, such as “yes/no”). If you only have a set of inputs with no outputs, and you want to identify different patterns in the inputs and cluster them according to similarities, you’ll want to use unsupervised learning algorithms. Also, it’s important to mention that for doing more complex tasks such as image classification, object detection, face recognition, machine translation, dialogue generation, etc., you will need more complex algorithms that fall in the deep learning category, which is based on artificial neural networks. 5. Services . Once you have defined the most appropriate machine learning model for solving a given problem, you’ll then need to decide whether to implement the model from scratch or use existing services. For example: • If you need to integrate nice conversational interfaces (chatbots) into any application using voice and text, AWS Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. • To uncover the insights and relationships in unstructured text data, AWS Comprehend helps you with: • Identifying the language of the text • Extracting key phrases, places, people, brands or events • Conducting sentiment analysis, to determine how positive or negative a text is • Automatically organizing a collection of text files by topic • If you want to integrate a neural machine translation service that delivers fast, high-quality, and affordable language translation into your app, take a look at AWS Translate. • If you want to add image and video analysis to your applications, AWS Rekognition can be an interesting option, because it provides an API for identifying objects, people, faces, text, scenes and activities, as well as for detecting any inappropriate content. Now, if you need to build your own machine learning models, and want a fully managed platform that allows you to quickly and easily build, train and deploy them into a production-ready hosted environment, AWS SageMaker is a great choice. 6. Deploying . For deploying machine learning solutions to AWS, you need to take into consideration key parameters, such as performance, availability, scalability, resiliency and fault tolerance. To that end, AWS provides solutions and best practices that will help you in the process. For example, you can enable monitoring on your solutions so you can check performance and scale your services up or down accordingly. You can even enable autoscaling so AWS takes care of that for you. You can also deploy your solutions to multiple availability zones to ensure maximum availability. 7. Security . As in every software solution, managing security for AWS machine learning solutions is a crucial task, especially because machine learning models need a lot of data to be trained, and access to that data should be provided to authorized people and applications only. The good news is that AWS has a specific service for this: The Identity and Access Management (IAM) service. Rock AWS Machine Learning . The role of a ML/AI Developer is becoming a strategic necessity for most organizations. As you can see, there are several skills that you need to make the biggest impact possible in this role. Dedicate yourself to the essentials and you can make it.  See how your AWS ML/AI developer skills measure up. Get your Role IQ.    Share About the author. Jorge is a passionate person who loves building quality software that allows people to solve their problems. He also loves to teach, that's why he works several years ago teaching programming and software development to university students. He has experience developing highly performant backend systems with Java and Node.js, building ETL processes with Python and Scala and working with cloud platforms such as Amazon Web Services. His current areas of interest include Deep Neural Networks, Computer Vision, Natural Language Processing and Reinforcement Learning. When not developing software or teaching, he enjoys photography and spending time with his family. Recommended articles 5 keys to successful organizational design How do you create an organization that is nimble, flexible and takes a fresh view of team structure? These are the keys to creating and maintaining a successful business that will last the test of time. Read more Why your best tech talent quits Your best developers and IT pros receive recruiting offers in their InMail and inboxes daily. Because the competition for the top tech talent is so fierce, how do you keep your best employees in house? Read more Technology in 2025: Prepare your workforce The key to surviving this new industrial revolution is leading it. That requires two key elements of agile businesses: awareness of disruptive technology and a plan to develop talent that can make the most of it. Read more Subscribe to the newsletter Back to blog › Ready to skill upyour entire team? 10 Subscriptions Need more subscriptions? Contact sales. Continue to checkout Continue to checkout Cancel With your Pluralsight plan, you can:. With your 14-day pilot, you can:. Access thousands of videos to develop critical skills Give up to 10 users access to thousands of video courses Practice and apply skills with interactive courses and projects See skills, usage, and trend data for your teams Prepare for certifications with industry-leading practice exams Measure proficiency across skills and roles Align learning to your goals with paths and channels Ready to skill upyour entire team? 10 Subscriptions Need more subscriptions? Contact sales. Continue to checkout Cancel With your Pluralsight plan, you can:. With your 14-day pilot, you can:. Access thousands of videos to develop critical skills Give up to 10 users access to thousands of video courses Practice and apply skills with interactive courses and projects See skills, usage, and trend data for your teams Prepare for certifications with industry-leading practice exams Measure proficiency across skills and roles Align learning to your goals with paths and channels Solutions . Pluralsight Skills Pluralsight Flow Government Gift of Pluralsight View Pricing Contact Sales Skill up for free Platform . Browse library Role IQ Skill IQ Iris Authors Professional Services Technology Index Company . About us Customer stories Careers Blog Newsroom Resource center Guides Resources . Download Pluralsight Events Teach Partners Affiliate Partners PluralsightOne.org Subscribe Support . 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                            [body] => Top 5 Skills Needed to be a Deep Learning Engineer! Facebook Twitter Linkedin Youtube Gourav Singh — June 28, 2021 Artificial Intelligence Beginner Career Cloud Computing Deep Learning Learning Path Listicle Profile Building This article was published as a part of the Data Science Blogathon “What’s behind driverless cars? Artificial Intelligence or more specifically Deep Learning”  – Dave Waters When Introduction. Hi everyone, in this article, I will clear some of the general doubts that everyone has while entering the field of Deep Learning and would tell you the technologies that you should learn to get a better understanding of the field, for this purpose my this article is not going to be too much formal and technical so you are free to ask any questions in the comment section I will be very happy to answer those. In India, around 700 thousand students graduate per year, and students who want to pursue their dreams in the Development side of Computer Science need to choose one domain, Deep Learning is one of them. As technologies are growing larger and larger students’ interest in Machine Learning/Deep Learning is also growing. One big issue that everybody faces is that they all know there is some good scope in the field of AI, but what they lack is where to start and in which direction they need to focus their energy, they try one technology and after a few days, they jump on the other one leaving the last one unpracticed. Most Deep Learning Practitioners are afraid that big tech giants are working on some algorithms that will automate the whole Deep Learning Process and there would be no such post as Deep Learning Engineer. So let me confirm you one thing that is not going to happen, not now for sure. Automation is taking over the place but there is still a long path to go. If you know that you are good with numbers and want to work on some exciting technologies, Deep Learning is surely for you, in this tutorial I will tell you the pathway which you can follow to be a successful Applied Deep Learning Engineer. What is Deep Learning? Deep Learning is the subset of Machine Learning that primarily deals with Neural Networks (NN). It provides solutions for almost all kinds of data like images, text, audio, etc. Neural Networks try to mimic the brain in order to produce results in the same fashion as the human mind does. This theory part you guys already know so let me jump straight to the things that you are sceptical about. I know you might have doubts about if you need a master’s degree or you need to be from Harvard or MIT to be a good fit for Deep Learning, let me answer that for you. There are two roles in Deep Learning as well one called a Deep Learning Researcher and another one as Applied Deep Learning Engineer. The first one deals with having more statistics and mathematics-based knowledge that can help you understand the Deep Learning concepts and would eventually lead you to discover new algorithms/technologies, while the other one deals with whatever is already implemented by Deep Learning Researchers just use that and apply it somewhere, where it can reduce the human effort. Source: Levity.ai Now you know that if you are not from some Tier-1 college you can still be a Deep Learning Engineer, so let’s discuss what all technologies you will need to be a successful Deep Learning Engineer. Summary of Skills Needed:. While you work on Machine Learning/Deep Learning it’s not like you will just have to know some algorithms and apply them to the data that you are gonna get. You will start from the requirement phase i.e. first identify the problem for which you will find the solution. One most important thing is not all problems require Deep Learning solutions first try to analyze the problem and see if it can be solved using traditional algorithms if yes you will be saving a lot of energy and resources otherwise you are free to choose Deep Learning solution. 1. A programming language suitable for AI/ML/DL. I know you might be wondering why I am telling you this while you might already know it, but choosing the programming language is the first task that sets you up on the path of Deep Learning. Common languages preferred for DL are Python and R (personally I use python). Source: Datacamp.com Both of these languages have their specialties it’s not like when you are using one you can completely ignore the other one, having knowledge of both of them is a cherry on the cake. When you start learning any of these programming languages try to focus fully on one language and once you have mastered it, another one would be very easy for you. Try to master as many libraries as you can, once done, it would be very easy for you to work on real-world projects. Problems Faced:. While anyone starts learning a programming language main issue he/she faces is the knowledge of resources that will abate the learning process. I have also faced the same issue while doing the same. How I have mastered the Python language is by completing primary lectures from learnpython.org and them completing several video lectures from Udemy and Coursera. One important suggestion that I would surely make is don’t watch video lectures just to earn certificates and fancy your linked profile, watch them, gain knowledge that would eventually help you to be a better developer. 2. Computer Science Fundamentals and Data Structures. Knowing Machine Learning/Deep Learning algorithms is not enough, you will also require knowledge of  Software Engineering skills like Data Structures, Software Development Life Cycle, Github, Algorithms (Sorting, Searching, and Optimisation). Source: reachcambridge.com When you work on any real-world project, the client would not need any Machine Learning model, what he would require is a solution in form of any Service or Application for that you need to have a deeper understanding of these concepts. Problems Faced:. Most of the Data Science enthusiasts think that if they are going to work in the field of AI/ML/DL they will just have to learn a bunch of algorithms that they are anyways going to get in some package, and other concepts are not that much important spacially Data Structure. So let me clear one thing for you when you work on any live project where you will have to optimize your code in order to make it memory and time-efficient, and whenever we talk about efficiency there comes the Data Structure. Also when you work on live projects you need to work on deadlines so to deliver projects to clients on time you need to have a proper understanding of SDLC. I also studied these concepts in college only and when I was working on POCs (Proof of Concepts) I also did not have a proper understanding of these concepts in real-world but as I have been part of few projects these concepts became clear to me, so if you know these concepts and are afraid that you don’t know how to apply these concepts in real-world, don’t worry that you will learn only when you will be part of any project. 3. Mathematics for Machine Learning. If you are a software Engineer you can easily code any solution but when it comes to Machine Learning you need to have an understanding of Mathematical and Statistical concepts that will help you analyze any algorithm and tune it according to your need. Source: theconversation.com For training and inference also you will need to have knowledge of concepts like Gradient Descent, Distance Matrics, Mean, Median and Mode, etc. Problems Faced: . According to me, this is the important thing that someone must learn before entering the field of Deep Learning. Most of us think that when most of the algorithms are already implemented and we are just going to apply them to different fields why learn mathematical concepts. So as I have experienced every time when you are going to work on any Deep Learning algorithm you will have to tune it according to your use case and for that, you need these concepts. I have worked on several projects and honestly 95% of the time these concepts came in handy for tuning the algorithm other 5% I have used algorithms as it is. 4. Front End/UI Technology & Deployment Services. When you have your Machine Learning solution ready you need to represent it to others in form of Some Charts or Visualisations because the person to whom you are explaining might not have the knowledge of these algorithms and what he would want is the working solution for his problem. So what can enhance this development process is the knowledge of any UI technology like Django, Flask and if when required, JavaScript, your Machine Learning code would be the backend while you will create a frontend for the same. Source: yashints.dev Once the whole solution is ready you need to deploy that solution somewhere for that you should learn technologies like Apache, Wamp, etc. Problems Faced: . This technology is a must for working on Deep Learning projects, if you are part of a very big organization there are dedicated frontend and backend developers so there is no worry if you work only on the backend part but if you are part of a small organization or a small team most probably you will have to handle both frontend and backend development. 5. knowledge of Cloud Computing platforms. As we are moving ahead in technology the amount of data is increasing immensely, you can not manage that data on your local server so one should move to cloud technologies. These platforms provide some very good services from data preparation to model development. Source: Forbs.com Some of these computing platforms have some Deep Learning based solutions that are State of The Art. The most preferred platforms are AWS and Azure and you can also try Google Cloud. These are the technologies that one should learn while working as a Deep Learning Engineer, of course, you can learn other technologies also but these are the must ones. Problems Faced:. Working on Cloud Computing might be hard as you will have to work on this tech while working on other 4 techs, but if you are interested in learning some extra stuff this is surely for Deep Learning Engineer. Resource:. You can use the following resources to start learning these technologies: Programming Language: https://www.learnpython.org/ Data Structure: https://www.geeksforgeeks.org/data-structures/ Mathematics for Machine Learning: https://www.coursera.org/specializations/mathematics-machine-learning FrontendTechnologies: https://www.djangoproject.com/start/, https://www.fullstackpython.com/flask.html Cloud Technologies: https://aws.amazon.com/getting-started/, https://docs.microsoft.com/en-us/learn/azure/ Thanks for reading this article do like if you have learned something new, feel free to comment See you next time !!! ❤️ The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion. Related. blogatoncareer advicedeep learning About the Author. Gourav Singh Applied Machine Learning Engineer skilled in Computer Vision/Deep Learning Pipeline Development, creating machine learning models, retraining systems and transforming data science prototypes to production-grade solutions. Consistently optimizes and improves real-time systems by evaluating strategies and testing on real world scenarios. Our Top Authors. view more Download Analytics Vidhya App for the Latest blog/Article . Previous Post Part 17: Step by Step Guide to Master NLP – Topic Modelling using pLSA Next Post Part 18: Step by Step Guide to Master NLP – Topic Modelling using LDA (Probabilistic Approach) Leave a Reply Your email address will not be published. Required fields are marked *. Cancel reply Top Resources. TOP 10 GitHub Repositories for Data Science Ayushi Gupta - Jan 06, 2022 Python Tutorial: Working with CSV file for Data Science Harika Bonthu - Aug 21, 2021 3 Interesting Python Projects With Code for Beginners! GAURAV SHARMA - Jul 18, 2021 10 Best Data Science Websites to Find Datasets for your.. CHIRAG GOYAL - Jan 05, 2022 × . We use cookies on Analytics Vidhya websites to deliver our services, analyze web traffic, and improve your experience on the site. By using Analytics Vidhya, you agree to our Privacy Policy and Terms of Use.AcceptPrivacy & Cookies Policy Close Privacy Overview. This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience. 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                            [body] => 7-Step Guide to Become a Machine Learning Engineer in 2021 Last Updated: 04 Jan 2022 GET NOW Spoiler Alert: Becoming a machine learning engineer can sound like a hard-to-reach goal but let us tell you the truth – it isn’t as hard as it seems. And yes, we’re talking to you - the person who’s reading this because they’re wondering what is a machine learning engineer, what does a machine learning engineer do, how to become a machine learning engineer, and, more importantly, whether they can pull it off.   Image Credit: Makeameme.org  So you are considering learning machine learning skills, and you’ve heard that becoming a machine learning engineer is the way to go. That’s very savvy because machine learning engineers are the Swiss Army Knife of the data world. Having that designation means you can build end-to-end machine learning solutions, which is a highly marketable skill set considering the fact that it has been the fastest-growing job title in the world since 2019.  But what does it actually take to achieve the designation of a machine learning engineer? Becoming a machine learning engineer looks like a daunting task because one needs to possess a versatile skill set with knowledge of more than one programming language. A true machine learning engineer is a unicorn. That is why many machine learning tools are available to make it easier. One cannot be a master of all the machine learning skills with equal competency. So while it’s absolutely ok to set the bar high, don’t be disappointed if you don’t have expertise in all the skills that a machine learning engineer can/may use. Every machine learning skill can be honed over time by working on diverse machine learning projects. Use the 7 step road map to a machine learning career to plan out your learning. So without a further do here is the 7 step guide that will answer your question on how to become a machine learning engineer. Recommender System Machine Learning Project for Beginners-1 . Downloadable solution code | Explanatory videos | Tech Support Explore Project Before we forget, we want to make sure you know about our end-to-end solved data science and machine learning projects that are designed to help any mid-career professional kick-start their machine learning career. Table of Contents. How to Become a Machine Learning Engineer in 2021? 1) Is now a good time to become a machine learning engineer? (2021 Update)   2) What is a machine learning engineer? 3) Machine Learning Engineer vs Data Scientist 4) How to Become a Machine Learning Engineer-Learn Machine Learning Skills 5) Build a Machine Learning Portfolio 6) Find the Best Machine Learning Jobs 7) Ace Your Machine Learning Interview Getting Started How to Become a Machine Learning Engineer in 2021? . With that out of the way, onward with the 7 steps to becoming a machine learning engineer! 1) Is now a good time to become a machine learning engineer? (2021 Update). Before you change careers, it is important to consider the path ahead. Can a career in machine learning offer you growth opportunities and stability? How favorable is the job market towards machine learning skills? How likely are you to get hired? These questions need to be answered especially in the wake of the 2020 pandemic as it has had a major impact on the economy and hiring trends. With that in mind, let’s take a look at the state of the machine learning industry in 2021 and beyond. Machine Learning Engineer – The Hype is Real. You’ll have observed that, no matter what’s going on in the world around us, machine learning is omnipresent in our lives. Whether we’re trying to read and reply to our emails,  scrolling through our Facebook news feed, binge-watching on Netflix, making a purchase on Amazon, or having a conversation with “Siri” to schedule an appointment – everything that we do today relies on machine learning. Behind this technology is a team of data scientists and machine learning engineers who have not only built smart applications but constantly maintain them to ensure these machine learning applications work flawlessly. Those who can build and deploy machine learning models have a crucial role to play in the data-driven world – and this is clearly reflected in the data science and machine learning job market. New Projects . AWS MLOps Project for ARCH and GARCH Time Series Models View Project End-to-End Speech Emotion Recognition Project using ANN View Project Deep Learning Project for Time Series Forecasting in Python View Project Build a Graph Based Recommendation System in Python View Project Build a Customer Churn Prediction Model using Decision Trees View Project Build Deep Autoencoders Model for Anomaly Detection in Python View Project ML Model Deployment on AWS for Customer Churn Prediction View Project Detectron2 Object Detection and Segmentation Example Python View Project Build an AWS ETL Data Pipeline in Python on YouTube Data View Project Build Portfolio Optimization Machine Learning Models in R View Project View all New Projects Machine learning engineer is a pretty hot job title right now, and one which is set to become even more popular beyond 2021. Glassdoor ranked it 17th in their top 50 jobs in America for 2021, stating 2977 new machine learning job openings. The World Economic Forum reported that AI, Machine Learning, and automation will power the creation of 97 million new jobs by 2025. According to LinkedIn as of February 8th, there are over 106K jobs worldwide that list machine learning as a required skill, and over 51K in the U.S. alone. The number of job roles in machine learning and artificial intelligence grew 344% between 2015 to 2018 (Indeed.com) - much faster than the average for all other tech job roles. According to Gartner, the business value created by AI and Machine Learning will reach $3.9 trillion in 2022.     But do these statistics still stand after the unpredictable twists and turns of the pandemic situation in 2020? In a word, yes; machine learning engineers seem to have weathered the storm relatively well. Machine learning engineers entered LinkedIn’s top 15 in-demand jobs for 2021, and we can see this continuing beyond 2021. One of the reasons for the growth in the AI and machine learning job market can be attributed to the COVID-19 pandemic where most of the businesses were forced to enter into the digital realm for the first time while other businesses were trying to strengthen and maintain their position. With an increased number of consumers spending more money and time online, machine learning has taken center-stage and has become an essential technology for building a world post-COVID. Recommended Reading:  The A-Z Guide to Gradient Descent Algorithm and Its Variants A Comprehensive Guide to Ensemble Learning Methods Data Analyst Salary 2021-Based on Different Factors How to Become an MLOps Engineer in 2021 ? Senior Data Scientist Salary : How Much Will You Make in 2021? 2) What is a machine learning engineer? Before you jump into learning machine learning skills, first off let’s get clear on what is a machine learning engineer actually. A machine learning engineer is a software engineer by profession and sits at the intersection of software engineers and data scientists but with a specialization in machine learning. The focus of ML engineers goes beyond particularly programming machines to perform specific tasks. They are responsible for the end-to-end implementation and optimization of machine learning algorithms. The final output of an ML engineer is a working software product ( not the visualizations or analytical insights created along the way), and the “audience” for this output are other software pieces that run with minimal or no human intervention. An ML engineer is responsible for handling the theoretical data science models and scaling them up for production levels so they can take a substantial amount of real-time data. To get a better understanding on who is a machine learning engineer, let’s look at what does a machine learning engineer does on a day-to-day basis. Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects What does a machine learning engineer do? Implement statistical analysis and machine learning into highly available and high performant production level systems to provide ease of access to users. Automate feature engineering, model training, and evaluation process. Enrich machine learning frameworks and libraries. Develop API’s or web services for serving the model outcomes to internal teams, stakeholders, or users. Train and re-train machine learning systems as and when required. Translate the machine learning models defined by data scientists from environments like Python and R notebooks to analytic applications. 3) Machine Learning Engineer vs Data Scientist. You might hear the terms data scientist and machine learning engineer used interchangeably but these are two different job roles. Talking at a high level, we are trying to differentiate between scientists and engineers and it is pretty much obvious that the two are different job roles. The job role of an ML engineer is very much close to that of a data scientist because both work with large amounts of data and require skills to perform complex data modeling. But this is where the similarities between the two job roles end and this sparks a debate on the topic of data scientist vs machine learning engineer. Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects There are several definitions floating around the Internet for both job roles but these two professionals work in collaboration with each other to realize a quick and effective delivery of business value. A data scientist produces meaningful insights usually in the form of reports or charts while a machine learning engineer develops self-running software to automate predictive machine learning models. An ML engineer’s job role is the subset of a data scientist’s job role. A machine learning engineer acts as a bridge between the model-building task of the data scientist and the development of production-ready robust machine learning platforms, systems, and services. A major difference between a data scientist and a machine learning engineer is that a data scientist asks “What is the best machine learning algorithm to solve a given business problem?” and tries to find an answer to the question by testing various hypotheses. On the contrary, an ML engineer asks “What is the best system to solve the problem?” and finds a solution by building an automated process that can be used for speeding up the testing of hypotheses. A machine learning engineer feeds data into the machine learning models defined by data scientists. Both data scientists and machine learning engineers are of vital importance throughout the life cycle of a big data project and work together harmoniously to complement each other. Recommended Reading: Top 50 NLP Interview Questions and Answers for 2021 Data Scientist vs Machine Learning Engineer – Unleashing the Differences. Data Scientist vs ML Engineer Machine Learning Engineer Data Scientist Forecast and make predictions based on historical data using various machine learning models. Deals with real-world complex data to produce actionable insights. Applies machine learning to build actual data products The job of a machine learning engineer is experimental. The job of a data scientist is exploratory. Key Skills required are – Knowledge of Supervised/Unsupervised ML algorithms, NLP, Computer Vision, Deep Learning, Knowledge of Python, Tensorflow, Keras, PyTorch, etc, Data Wrangling, API’s, Algorithm Deployment, Scaling on Cloud, and Basic Math and Statistic concepts for ML. Key skills required are -Statistical Skills, Programming Languages, Big Data platforms, Machine Learning, and Data Visualization. The major challenge for machine learning engineers is dealing with the algorithm’s complexity and its scalability. Thus, an ML engineer is required to know how to tune parameters. The major challenge for data scientists is dealing with the unavailability of data. Average Machine Learning Engineer Salary: $112691 Average Data Scientist Salary: $129,000 4) How to Become a Machine Learning Engineer-Learn Machine Learning Skills. The skills needed for a machine learning engineer are diverse. In order to build, deploy and evaluate machine learning models, ML engineers work with programming languages, machine learning frameworks, tools,  and libraries. Let’s take a look at each of the machine learning skills in detail, that machine learning engineers use in their day-to-day work - Explore Categories. Deep Learning Projects Neural Network Projects Tensorflow Projects H2O R Projects IoT Projects Keras Deep Learning Projects NLP Projects Programming and Computer Science Fundamentals. In the world of machine learning, programming languages are the building blocks that ML engineers use to develop machine learning algorithms. There are many programming languages like C++, Java, Python, R , Clojure, or even Scala. Choose any one programming language and master it. Remember, not knowing a programming language will never be a deal-breaker in your machine learning career because any programming language can be learned fast enough. We suggest you focus on learning Python as it has become the de-facto programming language for the machine learning community. You will find thousands of lines of Python code that you can inspire to develop machine learning systems. In fact, most of the machine learning tools and frameworks (Keras, Tensorflow, Pandas, Sci-Py, Num-Py, Sci-Kit) used by ML engineers to develop machine learning systems are open-source. Apart from learning to program, you will need to know the basics of computer science fundamentals such as computer architecture, data structures, searching and sorting algorithms, and how to compute the complexity of algorithms. When learning any programming language, these are the key points to learn - Master the ability to build specialized data structures like binary trees, linked lists, or prefix trees. Master the ability to make use of highly optimized vectorized operations rather than loops. Handling exceptions. Working with data structures like lists, maps, sets, dictionaries, and hands-on experience on when to use which data structure. Free access to solved code Python and R examples can be found here (these are ready-to-use for your Data Science and ML projects)   LINUX – The go-to OS for Machine Learning Engineers. It is not possible to imagine the machine learning ecosystem without Linux. Though Windows and Mac are also great alternatives but a successful machine learning engineer is required to know how to install Linux and other required python packages for ML, how to work with the Linux file system, and how to move or copy data from Linux OS. Be it speed or flexibility, Linux has it all that an ML engineer needs. Statistics and Probability. The nuts and bolts taken from the field of probability and statistics are needed for a machine learning engineer. Most of the common machine learning algorithms are an extension of statistical modeling procedure, For this reason, it is necessary to learn the basic concepts of probability and statistics like – Bayes net, Hidden Markov Models, Conditional Probability, Types of Distribution, Hypothesis testing, ANOVA, etc. Know Your Machine Learning Algorithms. You will find several existing machine learning APIs, libraries, and packages like Spark MLib, Sci-Kit learn, Tensorflow, Keras, H2O, Theano, etc, that provide standard implementations for almost all machine learning algorithms. However, applying any of the machine learning techniques requires selecting the right model (SVM, KNN, Decision trees, etc.), choosing the right learning method, and an in-depth understanding of hyperparameter tuning to understand how the parameters affect the learning process of an algorithm. ProjectPro’s innovative ML projects are a great way to get exposure to diverse types of machine learning problems and their nuances. Data Modelling and Model Evaluation. The goal of a machine learning engineer is to train the best performing machine learning model possible, using the structure of the dataset. An ML engineer should also know how to choose the right evaluation strategy and error measures for a machine learning model. ProjectPro’s machine learning projects are set up with a perfectly curated learning path to help you learn all the required skills you need to become a machine learning engineer in the industry. That means you could have a new machine learning engineer job before this year’s over. Get More Practice, More Data Science and Machine Learning Projects, and More guidance.Fast-Track Your Career Transition with ProjectPro 5) Build a Machine Learning Portfolio. The weakest part of most machine learning resumes is the lack of experience working on diverse machine learning projects. If this is your resume, focus on building an awesome portfolio by adding some interesting ML projects. Every ML engineer needs an online portfolio that showcases their ability to apply machine learning to real-world problems. Ideally, a machine learning portfolio could consist of freelance projects that you’ve worked on or any other interesting ML projects that you’ve gained hands-on experience with. Especially for people who are getting started in the industry, you’ll need to build a job-winning machine learning portfolio to become a machine learning engineer. One way to do that is ProjectPro, “the one-stop platform to do data science and machine learning projects.” If you’re new to learning machine learning, add a diverse set of projects to your portfolio that exhibits your expertise of machine learning skills such as NLP, Neural Networks, Distributed Computing, Data Modelling and Evaluation, Reinforcement Learning along with hands-on knowledge of machine learning tools and technologies like Python, R, TensorFlow, Keras, etc.  All interesting machine learning projects-whether for recruiters or gaining experience -count. 6) Find the Best Machine Learning Jobs. There are lots of great job portals like LinkedIn, Indeed, and Glassdoor where you should invest some time in finding the right machine learning job based on your skills. Apart from this, there are specific job portals like ML Jobs List , Relocate designed particularly for machine learning jobs. And, yes don't forget to read the complete machine learning job description because sometimes the job description may not seem like a perfect fit for your skills but when you read the complete machine learning job description then only you know it is the dream job you've been looking for. Get confident to build end-to-end projects. . Access to a curated library of 120+ end-to-end industry projects with solution code, videos and tech support. Request a demo 7) Ace Your Machine Learning Interview. Regardless of whether you’re attempting to land clients as a freelance machine learning engineer or you’re seeking a full-time machine learning job, here are some best practices to follow when preparing for a machine learning interview – Come Prepared for a Hands-on Coding Interview. Machine learning interview questions function slightly differently than some of the other interview questions that you may have answered in the past. Choose a programming language preferably Python or R, master it, and prepare yourself to answer any kind of practical questions by writing code in a programming language you’re comfortable with. Here’s a list of machine learning interview questions to get you started. Come Prepared to Talk About Your Specialized Machine Learning Skills. Yes, the beauty of a machine learning engineer is that they can handle the end-to-end development of a machine learning solution. But, every ML engineer has his own strengths, interests, and specialized skills. Chances are that the hiring manager will ask you whether you prefer working on NLP problems or love building deep learning models or have an affinity towards computer vision. Don’t be afraid to share your specialties, and show how you specialize in one skill versus the other. Access Data Science and Machine Learning Project Code Examples Expect a Whiteboard Data Challenge. Companies will ask you to whiteboard a business use case during your interview just to understand your thought process and how well you code and analyze real-world business problems. Make sure you’re prepared for solving a custom machine learning project. The hiring managers will set up a business problem they already are working on and might ask you to propose an optimal machine learning solution for the same. For example, say if you are being interviewed at Wayfair they might ask you broad questions like “How do we optimize our television advertising budget?”. The hiring manager here is expecting you to ask other relevant questions and initiate discussion on the various data sources you might need, the metrics you will need to track, and then talk about the machine learning algorithms that you can implement to solve the given problem. The best way to prepare for a whiteboard data challenge to ace the machine learning interview is to practice diverse machine learning projects and get exposure to as many datasets and machine learning concepts as possible. Free access to solved code Python and R examples can be found here (these are ready-to-use for your Data Science and ML projects)  Most Watched Projects . Linear Regression Model Project in Python for Beginners Part 1 View Project Orchestrate Redshift ETL using AWS Glue and Step Functions View Project Build an Azure Recommendation Engine on Movielens Dataset View Project Machine Learning Project to Forecast Rossmann Store Sales View Project Data Warehouse Design for E-commerce Environments View Project View all Most Watched Projects Getting Started. A machine learning model with 98% accuracy locked in a Jupyter notebook is of no use! Become a machine learning engineer and deploy it in production! All great machine learning engineers start the same way – with interesting machine learning project ideas. The question is, where do you begin? How do you go from having an innovative machine learning project idea to successfully implementing it? At ProjectPro we guide you through the early steps of learning and training a machine learning model across various domains, so you’ll gain knowledge of all the essential machine learning tools, skills, frameworks, and technologies to build a  successful machine learning career with longevity. Click here to view a list of 50+ solved, end-to-end project solutions in Machine Learning and Big Data PREVIOUS NEXT Relevant Projects. Machine Learning Projects Data Science Projects Python Projects for Data Science Data Science Projects in R Machine Learning Projects for Beginners Deep Learning Projects Neural Network Projects Tensorflow Projects NLP Projects Kaggle Projects IoT Projects Big Data Projects Hadoop Real-Time Projects Examples Spark Projects Data Analytics Projects for Students You might also like. Data Scientist Salary How to Become a Data Scientist Data Analyst vs Data Scientist Data Scientist Resume Data Science Projects for Beginners Machine Learning Engineer Machine Learning Projects for Beginners Datasets Pandas Dataframe Machine Learning Algorithms Regression Analysis MNIST Dataset Data Science Interview Questions Python Data Science Interview Questions Spark Interview Questions Hadoop Interview Questions Data Analyst Interview Questions Machine Learning Interview Questions AWS vs Azure Hadoop Architecture Spark Architecture Tutorials. Data Science Tutorial Jupyter Notebook Tutorial - A Complete Beginners Guide Best Python NumPy Tutorial for Beginners Tableau Tutorial for Beginners -Step by Step Guide MLOps Python Tutorial for Beginners -Get Started with MLOps Alteryx Tutorial for Beginners to Master Alteryx in 2021 Free Microsoft Power BI Tutorial for Beginners with Examples Theano Deep Learning Tutorial for Beginners Computer Vision Tutorial for Beginners | Learn Computer Vision Python Pandas Tutorial for Beginners - The A-Z Guide NumPy Python Tutorial for Beginners Hadoop Online Tutorial – Hadoop HDFS Commands Guide MapReduce Tutorial–Learn to implement Hadoop WordCount Example Hadoop Hive Tutorial-Usage of Hive Commands in HQL Hive Tutorial-Getting Started with Hive Installation on Ubuntu Learn Java for Hadoop Tutorial: Inheritance and Interfaces Learn Java for Hadoop Tutorial: Classes and Objects Learn Java for Hadoop Tutorial: Arrays Apache Spark Tutorial - Run your First Spark Program PySpark Tutorial-Learn to use Apache Spark with Python R Tutorial- Learn Data Visualization with R using GGVIS Neural Network Training Tutorial Python List Tutorial MatPlotLib Tutorial Decision Tree Tutorial Neural Network Tutorial Performance Metrics for Machine Learning Algorithms R Tutorial: Data.Table SciPy Tutorial Step-by-Step Apache Spark Installation Tutorial Introduction to Apache Spark Tutorial R Tutorial: Importing Data from Web R Tutorial: Importing Data from Relational Database R Tutorial: Importing Data from Excel Introduction to Machine Learning Tutorial Machine Learning Tutorial: Linear Regression Machine Learning Tutorial: Logistic Regression Support Vector Machine Tutorial (SVM) K-Means Clustering Tutorial dplyr Manipulation Verbs Introduction to dplyr package Importing Data from Flat Files in R Principal Component Analysis Tutorial Pandas Tutorial Part-3 Pandas Tutorial Part-2 Pandas Tutorial Part-1 Tutorial- Hadoop Multinode Cluster Setup on Ubuntu Data Visualizations Tools in R R Statistical and Language tutorial Introduction to Data Science with R Apache Pig Tutorial: User Defined Function Example Apache Pig Tutorial Example: Web Log Server Analytics Impala Case Study: Web Traffic Impala Case Study: Flight Data Analysis Hadoop Impala Tutorial Apache Hive Tutorial: Tables Flume Hadoop Tutorial: Twitter Data Extraction Flume Hadoop Tutorial: Website Log Aggregation Hadoop Sqoop Tutorial: Example Data Export Hadoop Sqoop Tutorial: Example of Data Aggregation Apache Zookepeer Tutorial: Example of Watch Notification Apache Zookepeer Tutorial: Centralized Configuration Management Hadoop Zookeeper Tutorial Hadoop Sqoop Tutorial Hadoop PIG Tutorial Hadoop Oozie Tutorial Hadoop NoSQL Database Tutorial Hadoop Hive Tutorial Hadoop HDFS Tutorial Hadoop hBase Tutorial Hadoop Flume Tutorial Hadoop 2.0 YARN Tutorial Hadoop MapReduce Tutorial Big Data Hadoop Tutorial for Beginners- Hadoop Installation Top 15 Latest Recipes. Linear Schedular Up-Down in hebel Linear Schedular Up in hebel Constant Scheduler and Exponential Scheduler in hebel Nesterov Momentum Updater in hebel What is Momentum Updater in hebel What is simple SGD Updater in hebel What is parameter Updater and its base class in hebel What is multi-task neural net in hebel What is the logistic Regression neural net in hebel Neural Network Regression model in hebel Neural Network Classification model in hebel What is abstract base class model in hebel What is the moniters in Hebel and its types What is Multitask Top layer in hebel What is linear regression layer in hebel Get Free demo of AWS project. × Get Demo Now CONTINUE Download the Interview Guide. × Go, Get That Dream Job! CONTINUE
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                            [body] => How to become a machine learning engineer: A cheat sheet by Alison DeNisco Rayome in Artificial Intelligence on October 4, 2018, 7:54 AM PST If you are interested in pursuing a career in AI and don't know where to start, here's your go-to guide for the best programming languages and skills to learn, interview questions, salaries, and more. Tips for how to become a machine learning engineer Watch Now Machine learning engineers--i.e., advanced programmers who develop artificial intelligence (AI) machines and systems that can learn and apply knowledge--are in high demand, as more companies adopt these technologies. These professionals perform sophisticated programming, and work with complex data sets and algorithms to train intelligent systems. More about AI. AI trends for 2022: Four predictions from experts at Sony Deloitte: How sensitive AI data may become more private and secure in 2022 AI to see stricter regulatory scrutiny starting in 2022, predicts Deloitte Artificial intelligence ethics policy (TechRepublic Premium) While many fear that AI will soon replace jobs, at this phase in the technology's development, it is still creating positions like machine learning engineers, as companies need highly-skilled workers to develop and maintain a wide range of applications. To help those interested in the field better understand how to break into a career in machine learning, we compiled the most important details and resources. This guide on how to become a machine learning engineer will be updated on a regular basis.SEE: Managing AI and ML in the enterprise (ZDNet special report) | Download the report as a PDF (TechRepublic)What is machine learning? According to TechRepublic writers Hope Reese and Brandon Vigliarolo, machine learning is a branch of AI that gives computer systems the ability to automatically learn and improve from experience, rather than being explicitly programmed. In machine learning, computers use massive sets of data and apply algorithms to train on and make predictions. Machine learning systems are able to rapidly apply knowledge and training from large data sets to perform facial recognition, speech recognition, object recognition, translation, and many other tasks. Additional resources What is machine learning? Everything you need to know (ZDNet)Top 5: Things to know about machine learning (TechRepublic)Report: The 10 most innovative companies in machine learning (TechRepublic)Five steps for getting started in machine learning: Top data scientists share their tips (TechRepublic)IT leader's guide to the future of artificial intelligence (Tech Pro Research)IT leader's guide to deep learning (Tech Pro Research)Why is there increased demand for machine learning engineers? Demand for AI talent, including machine learning engineers, is exploding: Between June 2015 and June 2018, the number of job postings with "AI" or "machine learning" increased by nearly 100%, according to a report from job search site Indeed. The percent of searches for these terms on Indeed also increased by 182% in that time frame, the report found. "There is a growing need by employers for AI talent," Raj Mukherjee, senior vice president of product at Indeed, told TechRepublic. "As companies continue to adopt solutions or develop their own in-house it is likely that demand by employers for these skills will continue to rise."SEE: IT jobs 2018: Hiring priorities, growth areas, and strategies to fill open roles (Tech Pro Research) In terms of specific positions, 94% of job postings that contained AI or machine learning terminology were for machine learning engineers, the report found. And 41% of machine learning engineer positions were still open after 60 days. "Software is eating the world and machine learning is eating software," Vitaly Gordon, vice president of data science and software engineering for Salesforce Einstein, told TechRepublic. "Machine learning engineering is a discipline that requires production grade coding, PhD level machine learning, and the business acumen of a product manager. Finding such rare people can uplift a company from a follower into a leader in their space, and everyone is looking for them." Additional resources Machine learning business use will double by end of 2018, report says (TechRepublic)AI investments will hit $232B by 2025, but businesses don't plan to cut jobs (TechRepublic) The 10 best tech jobs in America for 2018 (TechRepublic) The 10 IT jobs that will be most in-demand in 2020 (ZDNet)LinkedIn IDs machine learning as its most rapidly expanding job category (ZDNet)Google DeepMind founder Demis Hassabis: Three truths about AI (TechRepublic)What are some machine learning engineer job roles? Machine learning engineers can take a number of different career paths. Here are a few roles in the field, and the skills they require, according to Udacity. Software engineer, machine learning: Computer science fundamentals and programming, and software engineering and system design Applied machine learning engineer: Computer science fundamentals and programming, applying machine learning algorithms and libraries Core machine learning engineer: Computer science fundamentals and programming, applying machine learning algorithms and libraries, data modeling, and evaluation Additional resources The 6 most in-demand AI jobs, and how to get them (TechRepublic) 6 tips for integrating AI into your business (TechRepublic) Why AI could destroy more jobs than it creates, and how to save them (TechRepublic) By 2022, 1 in 5 workers will rely on AI to do their job (TechRepublic) Soon, machine learning agents behind every application (ZDNet) AI impact: Rethinking education and job training (ZDNet)What programming languages are best to learn to become a machine learning engineer? . Python and R are the most popular programming languages for machine learning, data science, and analytics, according to a KDnuggets survey. Python had a 66% share of voters who used the tool in 2018--an increase of 11% from 2017. Meanwhile, R had a 49% share in 2018, down 14% from 2017. An IBM report ranked Python, Java, and R as the top languages for machine learning engineers, followed by C++, C, JavaScript, Scala, and Julia.SEE: All of TechRepublic's cheat sheets and smart person's guides When developing machine learning applications, the training and operational phases for algorithms are different, as reported by our sister site ZDNet. Therefore, some people use one language for the training phase and another one for the operational phase. "For 'ordinary machine learning,' it does not matter what language you use," Luiz Eduardo Le Masson, data science leader at Stone Co., told ZDNet. "But when you need to have real online learning algorithms and inferences in realtime for millions of simultaneous clusters and respond in less than 500 ms, the topic does not only involve languages, but architecture, design, flow control, fault tolerance, resilience." Additional resources How to become a developer: A cheat sheet (TechRepublic) Five highly-paid and in-demand programming languages to learn in 2018 (TechRepublic) The 10 easiest programming languages to learn (TechRepublic) Which programming languages are most popular (and what does that even mean)? (ZDNet) The 10 coding languages top developers plan to learn next (TechRepublic) The 5 worst programming languages to learn in 2018 (TechRepublic) The 10 programming languages developers use most with Node.js (TechRepublic) The death of Ruby? Developers should learn these languages instead (TechRepublic) Programming languages: Python is hottest, but Go and Swift are rising (ZDNet) JavaScript rules but Microsoft programming languages are on the rise (ZDNet) How to learn programming: 3 languages to get you started (TechRepublic) Which programming languages pay best, most popular? Developers' top choices (ZDNet)What other skills are required to become a machine learning engineer? Generally, machine learning engineers must be skilled in computer science and programming, mathematics and statistics, data science, deep learning, and problem solving. Here is a breakdown of some of the skills needed, according to Udacity. Computer science fundamentals and programming: Data structures (stacks, queues, multi-dimensional arrays, trees, graphs), algorithms (searching, sorting, optimization, dynamic programming), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing). Probability and statistics: Formal characterization of probability (conditional probability, Bayes' rule, likelihood, independence) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models). Statistics measures (mean, median, variance), distributions (uniform, normal, binomial, Poisson), and analysis methods (ANOVA, hypothesis testing). Data modeling and evaluation: Finding patterns (correlations, clusters, eigenvectors), predicting properties of previously unseen instances (classification, regression, anomaly detection), and determining the right accuracy/error measure (e.g., log-loss for classification, or sum-of-squared-errors for regression) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation). Applying machine learning algorithms and libraries: Standard implementations of machine learning algorithms are available through libraries, packages, and APIs (such as scikit-learn, Theano, Spark MLlib, H2O, and TensorFlow). Applying them effectively means selecting the right model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models) and a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning. Software engineering and system design: Machine engineers are typically working on software that fits into a larger ecosystem of products and services. That means they need to understand how the different parts work together, communicate with the parts (using library calls, REST APIs, and database queries), and build interfaces for your piece that others can use. This involves knowing system design and software engineering best practices (including requirements analysis, system design, modularity, version control, testing, and documentation). Additional resources Research: Companies lack skills to implement and support AI and machine learning (Tech Pro Research)The AI, machine learning, and data science conundrum: Who will manage the algorithms? (ZDNet)Developer documentation: How to get it right (ZDNet) 10 ways that IT pros and developers can keep their tech skills up to date (TechRepublic) Six ways to fix the IT skills shortage (ZDNet)What is the average machine learning engineer salary? Machine learning engineers in the US earn an average salary of $134,449, according to data from Indeed. In terms of AI-related jobs, it comes in third place for salary, after director of analytics ($140,837) and principal scientist ($138,271). Additional resources The 10 highest-paying AI jobs, and the massive salaries they command (TechRepublic) The 10 best tech jobs that pay the highest salaries (TechRepublic) The 8 highest paid and most in-demand tech jobs in America in 2018 (TechRepublic) The 10 highest-paying gig economy jobs are all in tech (TechRepublic) Coding school doubles the salary potential for high school grads (TechRepublic)Where are the hottest markets for AI and machine learning engineer jobs? New York City has the highest concentration of AI jobs, with nearly 12% of all AI job postings found there, according to Indeed. New York also has the highest concentrations of data engineer, data scientist, and director of analytics job postings of any US metro area, potentially supporting the media, fashion, and banking industry centers located there, Indeed found. Following New York City in AI job concentration is San Francisco (10%), San Jose, CA (9%), Washington, DC (8%), Boston (6%), and Seattle (6%). San Jose has the most postings for machine learning engineers in particular, along with algorithm engineers, computer vision engineers, and research engineers. Additional resources The top 10 cities where job seekers want to move (TechRepublic) Here are the 15 best cities for women in tech (TechRepublic) The top 10 cities where tech workers can make the most money (TechRepublic) The 10 best cities to start your new career (TechRepublic)What are some typical machine learning engineer interview questions? Those applying for machine learning jobs can expect a number of different types of questions during an interview, testing their skills in mathematics and statistics, data science, deep learning, programming, and problem solving. Some questions that a machine learning engineer can expect to be asked during an interview include: What have you been working on for the past few years? What AI and machine learning tools are you familiar with, and how proficient are you in them? What do you do to stay on top of changing technologies? How do you clean and prepare data to ensure quality and relevance? How do you handle missing or corrupted data in a dataset? What are the ethical implications of using machine learning? It's also important for the job applicant to arrive at the interview with questions for the hiring manager, Dave Castillo, managing vice president of machine learning at Capital One told TechRepublic. "An interview is a two-way conversation," Castillo said. "Just as important as the questions that we ask are the questions that candidates ask us. We want to ensure that not only is the candidate the right choice for the company, but the company is the right choice for the candidate." Additional resources How Skype is making it easier to conduct technical and coding interviews (TechRepublic) 10 questions developers should ask employers during a job interview (TechRepublic) Throw out the whiteboard: 3 ways to improve the technical interview (TechRepublic) AI and machine learning take centre stage at Microsoft's student developer competition (ZDNet)Where can I find resources for a career in machine learning? There are different paths into a career as a machine learning engineer. A good place to start is by learning a programming language like Python, R, or Java. For machine learning specifics, a number of Massive Open Online Courses (MOOCs), online programs, and certifications are available, including classes on Coursera and edX, and a nanodegree from Udacity. You can also gain practical experience through doing real projects on real data, on sites like Kaggle. Joining local organizations such as meetups or hackathons to learn from others in the field can also help. Additional resources Facebook and Udacity want to give you a scholarship to master machine learning (TechRepublic)Photos: 20 best resources for learning how to code (TechRepublic)Coding school graduates: Are they worth hiring? (TechRepublic) 3 tips to spot a fraud coding bootcamp and choose the right one (TechRepublic) LinkedIn launches AI Academy to bolster internal AI skills (ZDNet) Veterans: Here's which coding bootcamps will accept your GI Bill (TechRepublic) 3 iOS apps for learning to code in bite-size lessons (TechRepublic) How Google's Grasshopper app can help professionals learn to code for free (TechRepublic) Executive Briefing Newsletter. Discover the secrets to IT leadership success with these tips on project management, budgets, and dealing with day-to-day challenges. Delivered Tuesdays and Thursdays Sign up today Also see. How to implement AI and machine learning (ZDNet special feature) | Download the PDF version (TechRepublic)Five ways your company can get started implementing AI and ML (ZDNet)5 tips to overcome machine learning adoption barriers in the enterprise (TechRepublic)Why AI and machine learning need to be part of your digital transformation plans (ZDNet)How GDPR will change the way we build machine learning algorithms (TechRepublic)CIO's guide to data analytics and machine learning (free download) (Google Cloud white paper) Image: iStockphoto/Gorodenkoff Productions OU Editor's Picks . TechRepublic Premium: The best IT policies, templates, and tools, for today and tomorrow. The best programming languages to learn in 2022. Check for Log4j vulnerabilities with this simple-to-use script. TasksBoard is the kanban interface for Google Tasks you've been waiting for. Paging Zefram Cochrane: Humans have figured out how to make a warp bubble. 2022 tech conferences and events to add to your calendar. Comment and share: How to become a machine learning engineer: A cheat sheet By Alison DeNisco Rayome. Alison DeNisco Rayome is a senior editor at CNET, leading a team covering software, apps and services. She was previously a senior editor at CNET's sister site TechRepublic. | Full Bio | Disclosure | See all of Alison's content Related Topics: Artificial Intelligence CXO Innovation Internet of Things Big Data Analytics Developer Artificial Intelligence on ZDNet Show Comments Hide Comments LOG IN TO COMMENT My Profile Log out | Commenting FAQs | Community Guidelines Join Discussion. LOG IN TO COMMENT Add your Comment
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                            [body] => Top 10 Skills You Need For A High-Paying Machine Learning CareerRobert SmithFollowJul 13, 2020 · 5 min readIf there is a way to silently revolutionize the industry, then we have to talk about Machine Learning. A concept that is changing the way things are working is also paving the way for a new career trajectory. Machine Learning experts are in great demand, and it is only going to increase with time. Let us give you some examples, Siri, Alexa, Voice Assistance, Automated Cars, and many others all have Machine Learning at the core. Because of the growing dependence on Machine Learning, we have seen companies going ahead with Machine Learning training.Here is a quick snap at some of the eye-catching data:. According to Gartner’s report, AI has shown 2.3 million jobs in Machine Learning would be available by the end of 2020.Indeed, another report shows that Machine Learning Engineering, Software Engineering, topped the list for the most sought-after skills.Companies like Apple, Google, and Amazon have invested millions of dollars in research on Machine Learning and developing its future projects.All this piece of information indicates that Machine Learning is going to be the hottest selling skills. But to excel as a machine learning expert, you must consider upskilling yourself so that you can become proficient in using ML concepts. If you join any Machine Learning training or certification course, you will learn about skills that will help improve your proficiency as a Machine Learning expert.Big Data jobsTen skills that you need for a high-paying Machine learning jobApplied mathematics- Mathematics plays a key role in Machine Learning, and if you wish to excel as a machine learning expert, then you must know about the mathematical skills. Some of the key concepts that you need to master are linear algebra, probability, statistics, multivariate calculus, algorithm, and optimization.Neural Network Architecture- the second concept that one needs to excel to become a Machine Learning expert is a neural network. These are a predefined set of algorithms to implement a machine learning task. Overall, it helps the machines to perform like the human brain.Data Modelling and evaluation- The next skill in our list is that of data modeling and evaluation. It helps in handling large volumes of data and assessing how the final system will work.Natural Language Processing- Machine Learning works on Natural Language Processing; it ensures that the machine works just like a human brain and interprets the same. Some of the techniques that you need to master while learning ML are Word2vec, Sentiment Analysis, Summarization, Gensim, and NLTK.Reinforcement Learning- It plays a key role in comprehending the conspectus of AI and deep learning, which are an integral part of Machine Learning. If you are going for machine learning for a beginner’s program, you must know about reinforcement learning.Distributed Computing- As an ML engineer, you would be required to work on a large volume of data, hence having complete knowledge about distributed computing is a must.Unix- ML engineers working on cluster data and servers are on Linux. Though a few ML engineers work on Windows and Mac, it becomes imperative that one must know about Unix and Linux systems.C, C++, and Java- As a magician learning expert, you must know programming languages. To understand the complexity of data and prepare an algorithm for machine learning, one needs to know C, C++, and Java.Spark and Hadoop- The next skill that will help you fetch a high-paying machine learning job is Spark and Hadoop. It is a framework for large scale implementation of Machine Learning.Rapid Prototyping- Choosing the right method of learning is imperative. Hence, a Machine Learning engineer needs to have good prototyping skills.These skill sets will put you in the league of the most skilled machine learning expert. Not only it helps you become a proficient ML engineer, but at the same time, you will get better pay. Upskilling oneself is the need of the hour. The more you start learning about such concepts, the better the prospects of growth in your career profile.Trending AI Articles:1. Machine Learning Concepts Every Data Scientist Should Know2. AI for CFD: byteLAKE’s approach (part3)3. AI Fail: To Popularize and Scale Chatbots, We Need Better Data4. Top 5 Jupyter Widgets to boost your productivity!A bonus tip that we would like to highlight here is that you need to work on your communication skills. It will help in the right presentation of information, and you would be able to make even a naïve client understand complex machine learning concepts.Concluding thoughts-. Machine Learning is silently impacting all past of our life, and this is also leading to an increased demand for machine learning experts. If you are also willing to become an expert in this field, Global Tech Council is offering a machine learning certification program. As a part of this program, you will learn about all the concepts of machine learning, along with its application.For more such updates and information on the machine learning training program, connect with the Global Tech Council today.Don’t forget to give us your 👏 !Becoming Human: Artificial Intelligence Magazine. Latest News, Info and Tutorials on Artificial Intelligence…Follow63 Machine LearningAIArtificial IntelligenceComputer ScienceMachine Learning Career63 claps63 Written byRobert Smith. FollowI am trying to give you different information about different fields like technology, software, finance, and education, etc.FollowBecoming Human: Artificial Intelligence Magazine. FollowLatest News, Info and Tutorials on Artificial Intelligence, Machine Learning, Deep Learning, Big Data and what it means for Humanity.FollowWritten byRobert Smith. FollowI am trying to give you different information about different fields like technology, software, finance, and education, etc.Becoming Human: Artificial Intelligence Magazine. FollowLatest News, Info and Tutorials on Artificial Intelligence, Machine Learning, Deep Learning, Big Data and what it means for Humanity.More From Medium. How a community accelerates your Machine Learning Journey. Kurian BenoyThe Sequence Scope: A New Release of PyTorch is Here. Jesus RodriguezRadiant Earth Foundation Releases the Benchmark Training Data “LandCoverNet” for Africa. Radiant Earth Foundation in Radiant Earth InsightsIn the Loop №4: No-Code Machine Learning. Avantika M in AIxDesignRemove haze in a single image by using three components. AI News Clips by Morris Lee: News to help your R&DSentiment Classification using Feed Forward Neural Network in PyTorch. Dipika Baad in The StartupSupporting the Math Behind Supporting Vector Machines!Daksh Trehan in Towards AIDoing ML Part 2: Classification. Ketan Pandey in DataDrivenInvestor
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Ad
discoverdatascience.org is an advertising-supported site. Clicking in this box will show you programs related to your search from schools that compensate us. This compensation does not influence our school rankings, resource guides, or other information published on this site. Got it!
Featured Schools. 
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                            [body] => How to Become a Machine Learning Engineer – A Complete Career Guide In This Guide:. 5 steps to become a machine learning engineerWhat is a machine learning engineer?What does a machine learning engineer do?Machine learning engineer job descriptionMachine learning engineer salaryMachine learning engineer job outlook We all know the basic premise of artificial intelligence. AI is how machines are able to learn from their environment and increase intelligence over time. Most people were introduced to the whole idea through Hollywood cinema, which has given us a bleak look at the scary potential of what could happen with AI in the wrong hands, but many watching 2001: A Space Odyssey decades ago were not aware that that artificial intelligence has been around, at least in simplified form, since the 1950s. AI is a broad field encompassing many different applications, and it is now being employed on a large scale in a variety of organizations. As defined today, machine learning is one subset of AI that works with big data applications and is accomplished through advanced mathematics and software programming.  By far the most prominent use of machine learning (ML) is in business. Customer-facing businesses of all types are employing machine learning to better understand customer tendencies and preferences and apply marketing and advertising strategies that accurately and effectively target these tendencies. The goal of machine learning is to program computers to accept real world data from real people utilizing technology and determine from that data the person’s likes and tendencies. These results are then employed to place the most relevant advertisements in front of the customers.  Facebook is probably the most publicly obvious example of a machine learning user. Account holders in Facebook have become all too aware of the ads being targeted directly at them for everything they do. And not just on Facebook. Buy something, or even search for it on Amazon and you’ll soon see an ad for that item on your Facebook account.  Artificial intelligence and machine learning have only begun to be widely used in the last 10 to 20 years. The recognition of what machine learning can provide in business is still spreading. Machine learning job growth is expected to be among the most rapid in any industry for the foreseeable future, so prospects are very bright.  ML engineering is not an entry-level career option. It takes years of experience in data science and software engineering, as well as an advanced college degree, to become a machine learning engineer. This guide provides an overview of the machine learning engineer role and lists the steps required to begin and maximize career success. Included is a detailed list of job responsibilities, background, education, and experience required to be successful professionals, as well as salary information, and the future outlook for the ML engineering job market.  What is a Machine Learning Engineer?  Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. Once an ML program is written, it must be “trained” before it is deployed in its intended use. Training is the process by which the machine learns. The programming utilizes algorithms that ingest training data supplied by a machine learning engineer, making it possible to produce more precise models based on that data. A machine learning model is the output generated after a machine learning algorithm is trained with data ingestion. Once trained, when a machine learning model is fed real-world data, it produces an output. A predictive algorithm will create a predictive model. When the predictive model is provided with data, it puts out a prediction based on the data that trained the model.  Through its training and iterative online learning, a machine learning model can vastly improve its understanding of the types of associations that exist between data elements. Due to their complexity and size, these patterns and associations would be easily overlooked by human observation. Machine learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data.  A machine learning engineer must understand each of these approaches, as well as how and in what situations to apply them. The four basic approaches utilized are supervised learning, unsupervised learning, reinforcement learning, and deep learning. The differences between these approaches lies in the data to be used to create a learning model. Supervised learning is used with data already supplied with labels, for example, identifying animals from new images. Unsupervised learning is used with unlabeled data and is often used to identify spam or junk emails using unidentified elements within the email. Reinforcement learning is similar to supervised learning in that it uses labeled data. However, reinforcement learning is done without the benefit of training data, instead improving its modeling via trial and error from real-world data. Deep learning incorporates neural networks to learn from data in an iterative manner. It is especially useful in learning patterns from unstructured data in applications such as speech and facial recognition.  A machine learning engineer must have an advanced knowledge of mathematics to recognize different types of data sets and be able to define at least rudimentary patterns and tendencies in the data. Utilizing a machine learning platform, such as IBM, Microsoft, Google and Amazon, an ML engineer must then utilize advanced programming techniques and algorithms to create a system capable of ingesting a particular type of data and turning it into the desired modeling output.  What does a Machine Learning Engineer do?  With advanced skills in mathematics, programming and data science, machine learning engineers evaluate data streams and determine how best to go about producing models that return polished information to meet an organization’s needs. Once the programs are written, ML engineers provide data to help the system learn how to interpret data and make predictions or draw conclusions. When the system has been sufficiently trained, it goes live in whatever setting is needed. Machine learning engineers then must monitor the system’s performance and evaluate the data being returned by the modeling, to ensure its accuracy. In smaller organizations, machine learning engineers often double as data scientists, but in larger organizations the two professionals work collaboratively to provide clean data and create an optimal machine learning system that data scientists will then utilize to deliver required data.  The tools of the trade are the machine learning platforms, which are then utilized as the foundation for complex programs that ingest data and learn how to make the most accurate identifications, predictions, or whatever other modeled output is required. Programming languages most commonly used include, but are by no means limited to the following:  Python Java C C++  JavaScript R Scala Julia  Machine learning engineers must also have strong familiarity with the standard algorithms utilized for programming and modeling. Customized algorithms are sometimes required, or just alterations to the standard algorithms, but knowledge of these algorithms across the four basic approaches (supervised learning, unsupervised learning, reinforcement learning, and deep learning) is critical. Some of the most widely used algorithms include the following:  Decision trees Naïve Bays Classifications Ordinary Least Squares Regression Logistic Regression Support Vector Machines  Ensemble methods Clustering algorithms Principal Component Analysis Singular Value Decomposition Independent Component Analysis  ML engineers must also record their processes and results and report findings to their own organizations, and sometimes outside stakeholders.  Ad discoverdatascience.org is an advertising-supported site. Clicking in this box will show you programs related to your search from schools that compensate us. This compensation does not influence our school rankings, resource guides, or other information published on this site. Got it! Featured Schools. School Name Level Program More Info Georgetown University Master Master of Science in Business Analytics Website Concordia University, St. Paul Master Master of Science in Data Analytics Website Johns Hopkins Master Online MS in Data Analytics and Policy Website George Mason University Master Online MS in Data Analytics Engineering Website Utica College Master Online MS in Data Science Website Capella University Bachelor B.S. in Data Analytics Website Southern New Hampshire University Bachelor B.S. in Data Analytics Website University of Scranton Master Online MS in Business Analytics Website Drake University Master Online Master of Data Analytics Website Northern Illinois University Master Online Master of Science in Data Analytics Website Five Steps to Become a Machine Learning Engineer. Step 1: Undergraduate Degree  As the primary knowledge requirements for a machine learning engineer are mathematics, data science, computer science and computer programming, an undergraduate degree for an aspiring machine learning engineer should ideally be in one of those disciplines. Alternate degrees in related fields, such as statistics or physics, can also be applicable. Machine learning engineers must also have a strong business acumen to understand the data needs of employers, so degrees in business can also be a good starting point, but it must then be supplemented with extensive technical training in the necessary sciences.  Step 2: Initial Career Options  A machine learning engineer is not an entry-level position, but where does anyone start who may have the goal of becoming a machine learning engineer? Here are a few possibilities.  Software Engineer Software Programmer Software Developer  Data Scientist Computer Engineer  Step 3: Earn a Master’s Degree and/or Ph.D.  An undergraduate degree alone will not be enough for the vast majority of machine learning engineer job openings. Master’s degrees in data science, computer science, software engineering or the like, and even a Ph.D. in machine learning would provide a great many options for machine learning engineers.  Step 4: Post-graduate Career Path   Additional education and experience will enable professionals to at least get their foot in the machine learning engineer door but will also provide other options. Management leadership positions will become available to those with experience and education, and of course strong leadership skills. There is also a good deal of research into AI and machine learning being conducted, largely by mega tech companies like Apple, Google and Microsoft. These research positions may very well determine the future of machine learning. Some organizations that can’t justify a full-time machine learning staff hire freelance machine learning engineers to build and implement particular ML systems, so freelancing can be a lucrative and flexible professional career path. And for those with a desire to teach the next generation of machine learning engineers, university faculty posts will of course be well within reach.  Step 5: Never Stop Learning  In any technical industry, particularly one advancing so rapidly and dramatically as machine learning, keeping up with the times is critical. Always be aware of and learn new algorithms, machine learning platforms, programming languages, machine learning libraries, etc. Take continuing education courses, obtain professional certifications, and develop a network of other machine learning engineer professionals.  Machine Learning Engineer Job Description  While the basic duties of a machine learning engineer may be largely similar from organization to organization, the details will vary substantially. This will depend on the nature of the organization, what its primary needs and goals are for machine learning, and the experience level of machine learning engineer sought. Details typically included in a machine learning engineer employee wish list include some of the following:  Work with Data Scientists and Business Analysts to frame problems in a business context Build data pipelines that pull data from various sources Build and maintain learning models and machine learning infrastructure Select appropriate datasets and data representation methods Design experiments and analysis methodologies that are statistically rigorous  Run machine learning tests and experiments Perform statistical analysis and fine-tuning using test results Build a user interface to interact with machine learning models through simulations, visualize model metrics and collect domain expert feedback Participate in code reviews to ensure code quality and share best practices and experiences with the team  Machine Learning Engineer experience and skills  Machine learning engineers are generally expected to have at least a master’s degree, and sometimes a Ph.D. in computer science or related fields. Advanced knowledge of mathematics and data analytical skills are critical components of a machine learning engineer’s background. Because processes and results must be communicated to management and/or outside stakeholders, ML engineers must also have strong written and oral communication skills. Specific experience and knowledge typically required by hiring employers may also include the following:  Experience with machine learning platforms such as Microsoft Azure, Google Cloud, IBM Watson, and Amazon Knowledge of machine learning programming languages such as Python, C, C++, Java, JavaScript, R, and Scala Knowledge and expertise with probability and statistics Knowledge and expertise with data modeling and evaluation  Experience with the application of machine learning algorithms and libraries Strong software engineering and development skills  Machine Learning Engineer Salary  The average annual salary of machine learning engineers is $112,806, plus bonuses and profit sharing, according to Payscale. Experience has a tremendous impact on the ML engineer’s earning power. On average, entry-level positions pay about $93,000 a year, while machine learning engineers with five to nine years of experience are paid about $138,000 per annum on average. And earning power doesn’t stop growing there. Machine learning engineers with 20-plus years under their belts make on average of $162,000 a year.  Machine Learning Engineer Job Outlook  The outlook for the field of machine learning is extremely high in today’s data science landscape. In fact, Forbes recently listed it as one of the Top 10 Tech Job Skills Predicted to Grow the Fastest in 2021. Forbes determined that demand for AI and machine learning skills will grow 71% compound through 2025 and estimated that there are almost 200,000 open positions today requiring a background in machine learning. Top industries machine learning engineers include manufacturing, information technology, finance and insurance, marketing and advertising for businesses, and professional services.  Programs Online Resources Related Programs We use cookies to ensure that we give you the best experience on our website. OkNo
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                                                            [name] => Free Course: Machine Learning Algorithms. 
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                                    [5] => stdClass Object
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