Copywriteroffice

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                            [title] => How Difficult Is Machine Learning? (2022 Guide) | BrainStation®
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                            [serp_description] => Although many of the advanced machine learning tools are hard to use and require a great deal of sophisticated knowledge in advanced mathematics, statistics, and software engineering, beginners can do a lot with the basics, which are widely accessible. ... To master machine learning, some math is mandatory.
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                            [body] => How Difficult Is Machine Learning?BrainStation’s Machine Learning Engineer career guide is intended to help you take the first steps toward a lucrative career in machine learning. Read on to learn more about how hard machine learning is. Become a Machine Learning Engineer. Speak to a Learning Advisor to learn more about how our bootcamps and courses can help you become a Machine Learning Engineer.Thank you!We will be in touch soon. Although many of the advanced machine learning tools are hard to use and require a great deal of sophisticated knowledge in advanced mathematics, statistics, and software engineering, beginners can do a lot with the basics, which are widely accessible. A variety of supervised and unsupervised learning models are implemented in R and Python, which are freely available and straightforward to set up on your own computer, and even simple models like linear or logistic regression can be used to perform interesting and important machine learning tasks. To master machine learning, some math is mandatory. Linear algebra, statistics, and probability form the foundation of machine learning. If you have serious plans to join the machine learning bandwagon, it’s time to brush up on your high school math. Apart from math, data analysis is the essential skill for machine learning. The ability to crunch data to derive useful insights and patterns form the foundation of machine learning. Loading a large data set, cleansing it to fill missing data, and slicing and dicing the data set to find patterns and correlation are the critical steps in data analysis. Even if you’re not the type of person who can instantly understand histograms, bar charts, line charts, and pie charts, you need to appreciate the power of visualization. Spend some time with Microsoft Excel to understand Pivot Tables and various visualization techniques available as charts. How to Start Learning Machine Learning. When it comes to machine learning, a little knowledge goes a long way. A variety of supervised and unsupervised learning models are implemented in R and Python, which are freely available and straightforward to set up on your own computer, and even simple models like linear or logistic regression can be used to perform interesting and important machine learning tasks. We should mention that many of the more advanced tools do require deep knowledge of advanced mathematics, statistics, and software engineering. Some key skills include an understanding of probability and statistics, complex linear algebra, and calculus in order to grasp the fundamentals of machine learning and easily work with data matrices. If you’re going to pursue machine learning, it’s a good idea to start with these key mathematical concepts and move onto the coding aspects from there. Many of the languages associated with artificial intelligence such as Python are considered relatively easy. For those of you already strong with math, the next step is picking the right machine learning framework. Essentially, there are a plethora of libraries to choose from when building out your model such as NumPy, Scikit-Learn, and Pandas. These toolkits range in difficulty depending on how advanced the project is. Find one simple framework to start with and grow from there. PreviousDoes Machine Learning Require Coding?NextHow Do I Learn AI? Get startedKickstart Your Machine Learning Engineer Career. We offer a wide variety of programs and courses built on adaptive curriculum and led by leading industry experts.Work on projects in a collaborative settingTake advantage of our flexible plans and scholarshipsGet access to VIP events and workshopsSpeak to a Learning AdvisorRECOMMENDED COURSES FOR MACHINE LEARNING ENGINEER. BOOTCAMPData Science BootcampThe Data Science bootcamp is an intensive course designed to launch students’ careers in data. CERTIFICATEPythonThe Python certificate course provides individuals with fundamental Python programming skills to effectively work with data. certificateData Science CourseTaught by data professionals working in the industry, the part-time Data Science course is built on a project-based learning model, which allows students to use data analysis, modeling, Python programming, and more to solve real analytical problems. We use cookies to improve your experience on our site, and to deliver personalized content. By using BrainStation, you agree to our privacy policy. I Accept ×
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                            [body] => Why is machine learning 'hard'? 7 minute read tweet hacker news discussion There have been tremendous advances made in making machine learning more accessible over the past few years. Online courses have emerged, well-written textbooks have gathered cutting edge research into an easier to digest format and countless frameworks have emerged to abstract the low level messiness associated with building machine learning systems. In some cases these advancements have made it possible to drop an existing model into your application with a basic understanding of how the algorithm works and a few lines of code. However, machine learning remains a relatively ‘hard’ problem. There is no doubt the science of advancing machine learning algorithms through research is difficult. It requires creativity, experimentation and tenacity. Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application. Engineers specializing in machine learning continue to command a salary premium in the job market over standard software engineers. This difficulty is often not due to math - because of the aforementioned frameworks machine learning implementations do not require intense mathematics. An aspect of this difficulty involves building an intuition for what tool should be leveraged to solve a problem. This requires being aware of available algorithms and models and the trade-offs and constraints of each one. By itself this skill is learned through exposure to these models (classes, textbooks and papers) but even more so by attempting to implement and test out these models yourself. However, this type of knowledge building exists in all areas of computer science and is not unique to machine learning. Regular software engineering requires awareness of the trade offs of competing frameworks, tools and techniques and judicious design decisions. The difficulty is that machine learning is a fundamentally hard debugging problem. Debugging for machine learning happens in two cases: 1) your algorithm doesn't work or 2) your algorithm doesn't work well enough. What is unique about machine learning is that it is ‘exponentially’ harder to figure out what is wrong when things don’t work as expected. Compounding this debugging difficulty, there is often a delay in debugging cycles between implementing a fix or upgrade and seeing the result. Very rarely does an algorithm work the first time and so this ends up being where the majority of time is spent in building algorithms. Exponentially Difficult Debugging. In standard software engineering when you craft a solution to a problem and something doesn’t work as expected in most cases you have two dimensions along which things could have gone wrong: algorithmic or implementation issues. For example, take the case of a simple recursive algorithm: def recursion(input): if input is endCase: return transform(input) else: return recursion(transform(input)) We can enumerate the failure cases when the code does not work as expected. In this example the grid might look like this: Along the horizontal axis we have a few examples of where we might have made a mistake in the algorithm design. And along the vertical axis we have a few examples of where things might have gone wrong in the implementation of the algorithm. Along any one dimension we might have a combination of issues (i.e. multiple implementation bugs) but the only time we will have a working solution is if both the algorithm and the implementation are correct. The debugging process then becomes a matter of combining the signals you have about the bug (compiler error messages, program outputs etc.) with your intuition on where the problem might be. These signals and heuristics help you prune the search space of possible bugs into something manageable. In the case of machine learning pipelines there are two additional dimensions along which bugs are common: the actual model and the data. To illustrate these dimensions, the simplest example is of training logistic regression using stochastic gradient descent. Here algorithm correctness includes correctness of the gradient descent update equations. Implementation correctness includes correct computations of the features and parameter updates. Bugs in the data often involve noisy labels, mistakes made in preprocessing, not having the right supervisory signal or even not enough data. Bugs in the model may involve actual limitations in the modeling capabilities. For example, this may be using a linear classifier when your true decision boundaries are non-linear. Our debugging process goes from a 2D grid to a 4D hypercube (three out of four dimensions drawn above for clarity). The fourth data dimension can be visualized as a sequence of these cubes (note that there is only one cube with a correct solution). The reason this is 'exponentially' harder is because if there are n possible ways things could go wrong in one dimension there are n x n ways things could go wrong in 2D and n x n x n x n ways things can go wrong in 4D. It becomes essential to build an intuition for where something went wrong based on the signals available. Luckily for machine learning algorithms you also have more signals to figure out where things went wrong. For example, signals that are particularly useful are plots of your loss function on your training and test sets, actual output from your algorithm on your development data set and summary statistics of the intermediate computations in your algorithm. Delayed Debugging Cycles. The second compounding factor that complicates machine learning debugging is long debugging cycles. It is often tens of hours or days between implementing a potential fix and getting a resulting signal on whether the change was successful. We know from web development that development modes that enable auto-refresh significantly improve developer productivity. This is because you are able to minimize disruptions to the development flow. This is often not possible in machine learning - training an algorithm on your data set might take on the order of hours or days. Models in deep learning are particularly prone to these delays in debugging cycles. Long debugging cycles force a 'parallel' experimentation paradigm. Machine learning developers will run multiple experiments because the bottleneck is often the training of the algorithm. By doing things in parallel you hope to exploit instruction pipelining (for the developer not the processor). The major disadvantage of being forced to work in this way is that you are unable to leverage the cumulative knowledge you build as you sequentially debug or experiment. Machine learning often boils down to the art of developing an intuition for where something went wrong (or could work better) when there are many dimensions of things that could go wrong (or work better). This is a key skill that you develop as you continue to build out machine learning projects: you begin to associate certain behavior signals with where the problem likely is in your debugging space. In my own work there are many examples of this. For example, one of the earliest issues I ran into while training neural networks was periodicity in my training loss function. The loss function would decay as it went over the data but every so often it would jump back up to a higher value. After much trial and error I eventually learned that this is often the case of a training set that has not been correctly randomized (it was an implementation issue that looked like a data issue) and is a problem when you are using stochastic gradient algorithms that process the data in small batches. In conclusion, fast and effective debugging is the skill that is most required for implementing modern day machine learning pipelines. tweet hacker news discussion Posted on: Thu 10 November 2016 Category: machine-learning © S. Zayd Enam. Built using Pelican.
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                            [title] => 5 Reasons You Don’t Need to Learn Machine Learning | by Roman Orac | Towards Data Science
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                            [body] => 5 Reasons You Don’t Need to Learn Machine LearningAn increasing number of influencers preach you should start learning Machine Learning. Should you listen to them?Roman OracNov 26, 2020·6 min readPhoto by Drew Hays on UnsplashAn increasing number of Twitter and LinkedIn influencers preach why you should start learning Machine Learning and how easy it is once you get started.While it’s always great to hear some encouraging words, I like to look at things from another perspective. I don’t want to sound pessimistic and discourage no one, I’ll just give my opinion.While looking at what these Machine Learning experts (or should I call them influencers?) post, I ask myself, why do some many people wish to learn Machine Learning in the first place?Maybe the main reason comes from not knowing what do Machine Learning engineers actually do. Most of us don’t work on Artificial General Intelligence or Self-driving cars.It certainly isn’t easy to master Machine Learning as influencers preach. Being “A Jack of all trades and master of none” also doesn’t help in this economy.Why do so many wish to learn Machine Learning?Photo by Ariana Prestes on UnsplashWhile I was studying at University, I’ve decided that I’m going to become a Machine Learning Engineer. It seemed hard, challenging and most importantly fun. Before that my wish was to become an iOS game developer.If someone would show me a workday from an ML engineer, maybe I would stick with iOS game development. Don’t get me wrong, I’m really happy with my career, but the career choice wouldn’t be as black and white as it was.Photo by KOBU Agency on UnsplashWhy is that? Because you can get the same amount of fun writing an iOS game as with training a Machine Learning model… or developing a backend application… or a frontend application. All of the above can become challenging (just ask engineers at top Tech companies).While in University, my thinking was:Machine Learning seems hard, so it's going to be easier to get a job. I’ll get higher wage. It is more future proof (web development will soon be automated) and it is fun.My thinking was wrong. So allow me to explain each of the statements above.1. Machine Learning seems hard. Photo by NOAA on UnsplashMost internet influencers preach: Starting with Machine Learning is really easy. You just download the Titanic dataset, copy 10 lines of Python code from a tutorial and you’ve started with Machine Learning.While that’s true, it’s hard to imagine that someone would pay you for that knowledge. So you need to go levels deeper.And levels deeper is where it gets hard. Having a great mentor is really important so that you don’t need to figure out everything on your own. Getting a good internship is also a great way to grow as an engineer.I wish someone would tell me that at the beginning of my career. I had to put in considerable hours to keep up with my peers who worked in other areas of Computer Science.Why? Well, It’s easier to get a mentor for frontend (backend or mobile) development because there’re more people doing it.2. Easier to get a Machine Learning job. Photo by Hunters Race on UnsplashOne thing is for sure and I learned it the hard way. It is harder to find a job as a Machine Learning Engineer than as a Frontend (Backend or Mobile) Engineer.Smaller startups usually don’t have the resources to afford an ML Engineer. They also don’t have the data yet, because they are just starting. Do you know what they need? Frontend, Backend and Mobile Engineers to get their business up and running.Then you are stuck with bigger corporate companies. Not that’s something wrong with that, but in some countries, there aren’t many big companies.3. Higher wages. Photo by Sharon McCutcheon on UnsplashSenior Machine Learning engineers don’t earn more than other Senior engineers (at least not in Slovenia).There are some Machine Learning superstars in the US, but they were in the right place at the right time — with their mindset. I’m sure there are Software Engineers in the US who have even higher wages.4. Machine Learning is future proof. Photo by Tomasz Frankowski on UnsplashWhile Machine Learning is here to stay, I can say the same for frontend, backend and mobile development.If you work as a frontend developer and you’re satisfied with your work, just stick with it. If you need to make a website with a Machine Learning model, partner with someone that already has the knowledge.5. Machine Learning is Fun. Photo by Braydon Anderson on UnsplashWhile Machine Learning is fun. It’s not always fun.Many think they’ll be working on Artificial General Intelligence or Self-driving cars. But more likely they will be composing the training sets and working on infrastructure.Many think that they will play with fancy Deep Learning models, tune Neural Network architectures and hyperparameters. Don’t get me wrong, some do, but not many.The truth is that ML engineers spend most of the time working on “how to properly extract the training set that will resemble real-world problem distribution”. Once you have that, you can in most cases train a classical Machine Learning model and it will work well enough.ConclusionPhoto by Johannes Plenio on UnsplashI know this is a controversial article, but as I already stated at the beginning, I don’t mean to discourage anyone.If you feel Machine Learning is for you, just go for it. You have my full support. Let me know if you need some advice on where to get started.But Machine Learning is not for everyone and everyone doesn’t need to know it. If you are a successful Software Engineer and you’re enjoying your work, just stick with it. Some basic Machine Learning tutorials won’t help you progress in your career.The aim of this article was to give a critical view that you usually don’t hear from influencers.Before you goHere are a few links that might interest you:- Labeling and Data Engineering for Conversational AI and Analytics- Data Science for Business Leaders [Course]- Intro to Machine Learning with PyTorch [Course]- Become a Growth Product Manager [Course]- Deep Learning (Adaptive Computation and ML series) [Ebook]- Free skill tests for Data Scientists & Machine Learning EngineersSome of the links above are affiliate links and if you go through them to make a purchase I’ll earn a commission. Keep in mind that I link courses because of their quality and not because of the commission I receive from your purchases.Follow me on Twitter, where I regularly tweet about Data Science and Machine Learning.Photo by Courtney Hedger on UnsplashRoman Orac. Senior Data Scientist. Get Unlimited Medium Reads: https://romanorac.medium.com/membershipFollow571 17571 571 17Machine LearningData ScienceWorkCareersArtificial IntelligenceMore 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. Time Series Forecasting of China Stock Market Using Weka-Part 5. Regression test for 399005. Harry zhengMy quarantine project: a real-time face mask detector using Tensorflow. Alberto Escarlate in Towards Data SciencePlaying around the Cartesian plane with the R programming language. Abrar Shariar in N-polygonNvesto, a new way to trade. IliaControls and their interpretation. Shanmukh DaraThe Pipeline of Airbnb Price Prediction. Nada GohiderWelcome to the Women Who Code Data Science Blog!Naomi Freeman in Women Who Code Data ScienceNapoleon 200 + Ho Chi Minh Nodal Ambition. Christophe Fernandez
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                            [description] => Despite the interest and a sense of urgency in embracing machine learning, developers are struggling to learn the essential skills required to master ML
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                            [body] => Why Do Developers Find It Hard To Learn Machine Learning?Janakiram MSVSenior ContributorOpinions expressed by Forbes Contributors are their own.CloudI cover Cloud Computing, Machine Learning, and Internet of ThingsThis article is more than 4 years old.Share to FacebookShare to TwitterShare to LinkedinMachine learning (ML) is touted as the most critical skill of current times. Artificial intelligence (AI), an application of ML, is becoming pervasive. From autonomous vehicles to self-tuned databases, AI and ML are found everywhere. Industry analysts often refer to AI-driven automation as the job killer. Almost every domain and industry vertical are getting impacted by AI and ML. Platform companies with massive investments in AI research are shipping new tools and frameworks at a rapid pace. Software Development Source: https://flic.kr/p/UBvMwx All the above factors have put an average developer under pressure to acquire machine learning skills. There is a sudden rush to learn the tools and technologies related to ML. The number of self-paced courses and MOOCs has doubled in 2017. In emerging markets like India, there are many technical training institutes promising to transform programmers into data scientists. Despite the interest and a sense of urgency, developers are struggling to learn the essential skills required to master ML. Here are some of the challenges that developers need to overcome before mastering machine learning. The Math Connection Let’s admit it – Most of us are scared of math. Software development did not enforce the direct application of math. The availability of reusable math libraries and functions relieved developers from doing math the hard way. An average programmer doesn’t get to deal with mathematics on a day to day basis. Only a few gifted developers have the natural intuition to math. To master ML, mathematics is mandatory. Linear algebra, statistics and probability form the foundation of machine learning. If you are a developer with serious plans to join the ML bandwagon, it’s time to brush up your high school math. It’s certainly a worthy investment. The Need to Analyze Data Apart from math, data analysis is the essential skill for machine learning. The ability to crunch data to derive useful insights and patterns form the foundation of ML. Like math, not every developer has the knack to play with data. Loading a large dataset, cleansing it to fill missing data, slicing and dicing the dataset to find patterns and correlation are the critical steps in data analysis. Even if you are not a person who can instantly parse histograms, bar charts, line charts and pie charts, you need to appreciate the power of visualization. Majority of data science deals with data preparation and analysis. Start spending time with Microsoft Excel to understand Pivot Tables and various visualization techniques available as charts. The Debate of Python vs. R vs. Julia Developers are often caught in the debate of using Python vs. R vs. Julia for developing ML models. The choice of a language is religious and best left to an individual. But if you are a beginner willing to learn one of these languages from scratch, it may get confusing. Python seems to be winning the battle as the preferred language of ML. The availability of libraries and open source tools make it an ideal choice for developing ML models. Though R is preferred by traditional statisticians, Python is recommended for most of the developers. Languages like Julia are gaining popularity, but it is Python that has the best data science ecosystem. The Fragmentation of Frameworks Even if you are a gifted math wizard with fantastic programming skills, one of the most significant challenges is choosing the right ML framework. Today, developers will have to choose from a variety of frameworks and libraries to build ML models. There are Python modules like NumPy, Pandas, Seaborn, Scikit-Learn followed by open source toolkits such as Apache MXNet, Caffe2, Keras, Microsoft Cognitive Toolkit, TensorFlow and PyTorch. It’s often confusing to a developer on choosing the right module and toolkit. If you are a Python developer, start with Scikit-Learn to build basic models before exploring advanced toolkits such as a Caffe2 and Keras. Most of these open source tools are meant for deep learning, which is an advanced technique of machine learning. The combination of Python and Scikit-Learn provides enough abstraction for developers to get started with the ML journey. Multiple Approaches to Solve the Same Problem After learning how to use the tools and modules, developers grapple with the confusion of choosing a specific algorithm to solve an ML problem. Machine learning comes with a set of predefined recipes called algorithms that are best suited for solving a particular problem. For example, choosing between Logistic Regression and K-Nearest Neighbor algorithm can be confusing to a beginner. Like most of the branches in computer science, ML offers multiple techniques to solve the same problem. Developers should learn the core concepts related to the algorithms and use their intuition in applying it to a given problem. In many cases, multiple algorithms are used to evaluate the precision and accuracy of a model before settling for one. Lack of Development and Debugging Tools The advancement in integrated development environments (IDE) enabled programmers to focus on the business problem than dealing with the configuration of the environment. Tools such as Eclipse, Microsoft Visual Studio and IntelliJ IDEA deliver out-of-the-box development and debugging experience to developers. Programmers can quickly set a breakpoint in the for loop to visualize the state of a variable that changes with every iteration. The developer experience delivered by the tools accelerated the process of shipping software. Unfortunately, existing developer tools are not ready for machine learning. Developers switch to an entirely different toolchain for developing ML models. Though tools such as Jupyter Notebooks are robust and mature, they are fundamentally different from traditional developer tools. Debugging an ML model is extremely hard when compared to a traditional program. Stepping through the code written to create a deep learning network is very complicated. IDE vendors such as Microsoft are working towards making the tooling experience seamless for ML developers. But it pays off to master Jupyter Notebooks to develop interactive Python applications. Too Many Learning Resources The number of self-paced courses and massive open online courses (MOOC) exploded in the recent past. There are dozens of courses available for developers to learn data science and machine learning. But the choice of these courses leads to confusion. Given how vast the ML domain is, no course is complete. The tools and frameworks are rapidly evolving making these courses outdated. Social media and blogosphere are full of articles, tutorials and guides related to ML. The challenge with this is that most of them are incomplete leaving the essential part to the imagination of the developer. It’s better to choose a single course or a guide to master a concept than referring to multiple resources. The overlapping and conflicting content is overwhelming and even misleading.   Follow me on Twitter or LinkedIn. Check out my website. Janakiram MSVPrintReprints & Permissions
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                            [body] => Is machine learning hard? Not always Ben McRedmond Senior Director of Growth, Intercom @benmcredmond June 22, 2018 Main illustration: Andy J Miller Share this article TwitterTwitter Icon Share this page on Twitter - this link opens in a new window LinkedInLinkedIn Icon Share this page on Twitter - this link opens in a new window Share this page on Twitter - this link opens in a new window There’s a common misconception that you have to be an AI wizard or mathematician to use machine learning in your work. That machine learning requires hard calculus. After all, you’re teaching machines that work in ones and zeros to reach their own conclusions about the world. You’re teaching them how to think! But like many frameworks we have for understanding our world – Newton’s Laws of Motion, Jobs to be Done, Supply & Demand – the fundamental ideas at the heart of machine learning are relatively straightforward. Our own machine learning expert, Fergal Reid, describes it this way: “You have a problem, you’re trying to solve it, and then you have a system where the performance improves when you give it more training data…The more data you get, the better your estimate.” If you’ve got a well-defined problem that can be solved given enough time and example data, you should give machine learning a try. Here’s an example. Example problem – without using machine learning. Say we want to include a “You might also like” section at the bottom of this post. How should we go about doing this? If you’re avoiding machine learning at all cost, you might opt for this approach: Split the current post title into its individual words. Get all other posts. Sort all other posts by those with the most words in their body in common with our title. Or, in Ruby: But look at the results you get when you apply this method to our blog post “How the support team improves the product”: How to launch with a validated idea Know your customers and how they decide Designing first run experiences to delight users How to hire designers The dribbblisation of design An interview with Ryan Singer Why being first doesn’t matter Proactive customer support with Intercom An interview with Joshua Porter Retention, cohorts, and visualisations We can do better — posts about running an effective support process have little in common with cohort analysis, or debate around the merits of design. The same example using basic machine learning. Let’s try a machine learning approach. We’re going to break this into two parts: Represent posts mathematically. Cluster these mathematical representations with K-Means. 1. Representing posts mathematically. If we can represent our posts mathematically, we can plot the posts, compare distances between posts, and identify clusters of similar posts. Mapping each post to a mathematical representation is easy. We can do it in two steps: Find all words in all posts. Convert each post into an array. Each element is a 1 or a 0, denoting presence of a word. This array is of the same order for each post, as it’s based off step #1. Or, in Ruby: If @words equaled: ['hello', 'inside', 'intercom', 'readers', 'blog', 'post'] A post with the body “hello blog post readers” would be mapped to: [1,0,0,1,1,1] We don’t have simple tools for plotting vectors in 6-dimensions, like we do for those in 2-dimensions — but concepts like distance are easily extrapolated. (It’s also still useful to use the 2-dimensional example.) 2. Clustering posts with the K-Means algorithm. Now we have a mathematical representation of our blog posts, we can find clusters of similar posts. For our problem, we’ll use a simple clustering algorithm called K-Means: Set ‘K’ to the number of clusters you want. Choose ‘K’ random points. Assign each document to its closest point. Choose ‘K’ new points, from the ‘average’ of all documents assigned to each point. Repeat steps 3-4. Until documents’ assignments stop changing. Let’s visualize these steps. First, we choose 2 (i.e. k = 2) random points, in the same space as our posts: We assign each document to its closest point: We re-evaluate the center of each of these clusters, to be the average of all posts in that cluster: That’s the end of our first iteration. Now we re-assign each post to its new closest point: We’ve found our clusters! We know this because it’s obvious in further iterations that the assignments would not change. Or, in Ruby: Here are the top 10 posts similar to “How the support team improves the product” produced with this method: Are you being clear, or clever? 3 rules for customer feedback Asking customers what you want to hear Shipping is the beginning of a process What does feature creep look like? Getting insight into your userbase Converting customers with the right message at the right time Conversations with your customers Does your app have a message schedule? Have you tried talking to your customers? The results speak for themselves. Now, with more statistics, we can keep optimizing the algorithm and improve our results, but this isn’t a bad start for roughly 40 lines of code! Other problems may require different clustering algorithms. Give it a try. Machine learning has led to breakthroughs in highly technical areas like computer vision, audio recognition, and natural language translation. But machine learning isn’t only just applicable to large abstract problems. It’s great at generating suggestions to help users with different workflows. Want to add tags in your project management app? Or assignees in your customer support tool? Or members of a group on a social network? Chances are an easy algorithm can help you out. So, when faced with a challenge in your product where you believe machine learning can help, don’t feel you have to master the math behind complex algorithms before giving it a try. These resources can help you get started: Relatively high-level programming libraries like Python’s Scikit Learn Books written for programmers like Programming Collective Intelligence by Toby Segaran Online courses like Andrew Ng’s Coursera course Machine learning might be more applicable and doable than you think. With thanks to Fergal Reid for his input We like to break things down to their fundamental principles. If that’s the way you like to work too, join our team Most Popular. Product & Design 6 min read RICE: Simple prioritization for product managers . Prioritization is a perennial challenge when building a product roadmap. How do you decide what to work on first? If… Sean McBride Former Product Manager, Intercom Support 8 min read The art of the customer follow-up and delightful customer service . Proactive customer follow-up is key to providing your customers with a sense of delight and a positive experience of your product or service. Colin Boylan Customer Support Representative, Intercom Product & Design 6 min read Continue, Stop, Start: Rethinking retrospectives . Running retrospectives is a common challenge for many teams. We’ve rethought the widely used “Stop, Start, Continue” model, focusing first on continuing the processes that already make our teams successful. Here’s why we use “Continue, Stop, Start” at Intercom. Kuba Niechciał Director, Engineering, Intercom
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                            [body] => Is machine learning difficult to learn? May 12, 2019 Dan Uncategorized If you are looking to learn machine learning then you might be wondering whether or not it is difficult to learn. This post will show you what goes into learning machine learning and how you can go about learning it. So, is machine learning difficult to learn? Machine learning requires the knowledge of calculus, linear algebra, probability, statistics and programming. It is not something that can be easily learned overnight but there are many MOOCs available that will guide you through the process. There are actually a number of different disciplines that machine learning makes use of but the truth is that anyone can learn machine learning if they are willing to put in the time and effort in doing so. Why people find it difficult to learn machine learning. People will often start trying to learn machine learning and quickly get discouraged when they realize that there is actually a lot of prerequisite knowledge that is required. Machine learning makes use of a number of different subjects including mathematics, statistics, probability, and programming. If you don’t have a good understanding of these subjects when you start learning machine learning then it will be difficult to follow what is going on. This is because the material will assume that you have an understanding of these topics. Despite that, if you take the time beforehand to get a good understanding of the prerequisites then it will make learning machine learning much easier and not too difficult. Additionally, there are some machine learning courses that don’t assume that you have any of the prerequisite knowledge. One of the most popular courses on machine learning which is taught by Andrew Ng doesn’t assume any prerequisite knowledge from you. It teaches you the theory that goes into the different machine learning algorithms. If you are looking to learn machine learning then I would recommend that you start there. How long will it take to learn machine learning? The machine learning course by Andrew Ng is estimated to take 55 hours to complete according to their website. However, a big part of making use of the different machine learning models will involve preprocessing and analyzing data. This is so that the machine learning models will work with the data and so that you know which model is appropriate. The Andrew Ng course doesn’t go into much detail there so it will also be necessary for you to learn a programming language and how to do data analysis in that language. The programming language that I would recommend for machine learning would be Python and the free course that I would recommend to learn Python with would be this one. It takes 9 weeks to complete and you should expect to spend 15 hours a week on the course. After that, I would recommend this youtube series to learn data analysis in Python. Having said that, the amount of time that it will take you to learn machine learning will depend largely on what you want to do with it and your background. If you already have a good understanding of programming, probability, statistics and mathematics then it will just be necessary for you to learn the different machine learning algorithms and how to use them. In this case, it would just be necessary for you to take a machine learning course or to read a machine learning book and then to start working on machine learning projects yourself. However, if you don’t know those subjects then it will likely take longer for you since you’ll be needing to learn them as well. How to learn machine learning. There are a number of different ways that you can go about learning machine learning. My advice would be to start by learning from a machine learning course that doesn’t have prerequisites such as Andrew Ng’s course and to learn to program in Python using this course while you are taking it. After that, I would suggest that you learn how to do data analysis in Python using this playlist. Then I would suggest that you learn from a practical machine learning course or book such as Hands on machine learning. Now, I would say that the best thing for you to do would be to apply your knowledge on your own projects and to do some of the competitions on Kaggle. Once you have a good understanding of probability, linear algebra and calculus then I would recommend that you take a look at Machine learning with Python by MIT. The course is a 15-week course and you should expect to spend 10-14 hours each week on it. The course will give you a much more detailed understanding of the mathematics behind the different machine learning and deep learning algorithms. Consider the way that you learn best. There are a wide variety of different machine learning resources that you can use to learn from and it would be helpful for you to consider the way that you learn best. If you learn best from books then there are a number of different books that you can get on machine learning, programming, data analysis and mathematics. The advantages of using books are that you’ll be able to go at your own pace, they are yours to keep and they will normally go into a lot of detail. However, some of the code in books related to programming will not be up to date unless the book was released recently. There are also many online courses such as the ones that are referenced above that you can use to learn machine learning. The online courses tend to be very well structured, interactive and will allow you to learn from some of the worlds top universities without actually going there. Apply the models to real world problems . Once you have learned the theory behind machine learning and how to implement the different machine learning algorithms it will be very useful for you to start doing projects of your own. One of the best ways to learn is by doing active learning where you are having to figure out and remember things yourself and many people say that they learned machine learning best when they started to tackle their own machine learning projects. A good way to find datasets that you can use to practice machine learning with is to use the website Kaggle.com. On there you will be able to find hundreds of different datasets relating to many different fields that you can use yourself. It also features machine learning competitions, with prize money, where people compete against each other to get the best score by applying machine learning algorithms to certain datasets. Have patience . When learning machine learning it is important to take a longer-term approach and not to expect to learn it overnight without any challenges. Unless you have a strong background in all of the prerequisites, which most people do not, then you’ll likely bump into some hurdles on the way. However, if you stick with it then you’ll come out with a very rewarding and useful skill that is currently highly sought after that is in a field that is constantly making new discoveries. report this ad report this ad xx
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                            [title] => Am I too stupid for machine learning? : learnmachinelearning
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                            [description] => 46 votes, 30 comments. I just started with the book Hands-On Machine Learning with Scikit-Learn & Tensorflow and am completely unable to understand …
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                            [serp_description] => I just started with the book Hands-On Machine Learning with Scikit-Learn ... in my opinion these were wayyyyy harder than chapters 4-9.
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                            [body] => Am I too stupid for machine learning? Close46Posted by4 years agoAm I too stupid for machine learning? I just started with the book Hands-On Machine Learning with Scikit-Learn & Tensorflow and am completely unable to understand what the author is trying to say. Pretty much after chapter one I was just like "Huh? What's going on here?".Did anyone have similar problems and can you recommend any softer starter books for machine learning?I was very eager to learn machine learning but the past 2 days have been rather demotivating.Edit: Thanks so much for all your input. As I see, my problem is that I currently do not know enough python (especially numpy, pandas etc....) I will pick up "Data Science from Scratch" first and then proceed to watching Andrew Ng's online course. If you have any further inputs just let me know ;)30 commentssharesavehidereport86% UpvotedLog in or sign up to leave a commentLog InSign UpSort by: best level 1 · 4 yr. ago · edited 4 yr. agoIt's already been said by others, but math really is cumulative. But you may not understand just HOW cumulative until you spend some time really digging in. Let me ask you this... have you ever spent time with another language? If you were to try and learn French, or Spanish, how do you think it would go? There's a huge up front learning cost before you can start talking comfortably, or reading, or watching shows or whatever. There's thousands of words to get down, new grammar concepts, all kinds of stuff.The thing is though, with language it's easy to understand that that's how it is. You don't sit down, struggle with an easy German book, and look over at the guy next to you reading Nietzsche and assume 'I'm bad at German'. No one would do that, it's assumed that someone with an extra 1,000 hours of practice would be a long ways ahead.But with math, there's this strange cultural perception that you're 'good at math' or 'bad at math'. I call horseshit on that. Math is a language of puzzles, and just like Chess or any other language, it takes time to acquire the vocabulary. Honestly, one of my turning points was playing through Jonathan Blow's 'the witness'. It's an incredible exploration of what it means to learn, and what logical creativity looks like. The whole thing is a Myst style island with hundreds of line puzzles, and it's meticulously put together so that absolutely no explanation is needed anywhere. The reason I'm bringing it up... we've decided with math you're 'good at math' or 'bad at math' and people who are good at it are 'those people' and meanwhile you're not one of them. But if you put yourself on equal footing with new puzzles and new rules, and watch for yourself just how well you're able to put pieces together, and (for that matter) how interested you actually are in the process of puzzle solving itself... it might help you reframe who you are, same as it did for me.So, it's true. ML assumes a ton of knowledge. Even hitting a basic understanding of what some of basic measures are (MSE vs RMSE vs MAE? SSE? RSS?) can be kind of non-trivial, especially when talking about some of the implications. I've been working through Hogg's introduction to mathematical statistics, and it literally spends the first 200 pages of dense math working up a framework to start looking at and talking about statistical problems. There's all kinds of ML shit that's thrown around like 'loss functions' and so on that might seem kind of made up until you ground them in the historical setting they emerged from. So... if you actually want to learn this, it's going to be the same as starting German. Expect time to start to get a deep understanding. Though if you'd like some early wins without knowing what the hell you're doing or why things worked, fast.ai is a great place to start. You can loop back around and hit the theory after you've gotten some successes under your belt.Just out of curiosity, what in the hands on book, where'd you get stuck?84ReplyShareReportSaveFollowlevel 2 · 4 yr. agoIt's also interesting to note that in math, it often isn't just cut and dry, one subject separate from the rest. What I mean by this is that the more math you look at and understand, the more concepts you see creep into other parts of mathematics. And the more that happens, the quicker you can pick things up. For instance, I was in a proof course and saw ways to relate the new information I was learning because I saw some stuff from linear algebra. It wasn't directly LA, but the way the concept was presented that made it easy to relate to. So I was able to go and take what seemed like a completely separate knowledge base, and apply it to what I was learning, and picked it up almost instantly. But that was only because I had learned other concepts. In the beginning, that is not easy. But the more you spread out your knowledge, literally the easier it gets. It is exactly the same with machine learning and it's a bit recursive: the more you learn, the easier it is to learn more.4ReplyShareReportSaveFollowlevel 2 · 1 mo. agoJust wanted to thank you for such a well written response 3 and a half years later. So many people could benefit from this sort of rationale regardless of what their motive is.Because of you I am inclined to take the first step of what will hopefully be many on my way to spend the rest of my life learning ML :)1ReplyShareReportSaveFollowContinue this thread level 1 · 4 yr. agoHands-On Machine Learning goes a little deep a little fast.Check out "Data Science from Scratch" by Joel Grus. It's a similar format, but it'll give you the prerequisites to tackle Hands-On ML. This is the path I took.12ReplyShareReportSaveFollowlevel 2 · 4 yr. agoThanks for this!2ReplyShareReportSaveFollowlevel 2Op · 4 yr. agoWill check it out... thanks2ReplyShareReportSaveFollowlevel 1 · 4 yr. agoIt's been slow going for me as well, with 20 years of software development since my last math class. I've found videos to be a much clearer way to learn, especially ones like https://www.youtube.com/watch?v=WCUNPb-5EYI and the rest of the https://www.youtube.com/user/BrandonRohrer/videos list.The worst of it has been when I've been stuck for two weeks, off and on, at one point, unable to move forward because I couldn't get the results I was expecting, could find out WHY I wasn't getting the results I was expecting, and completely questioning my understanding of all the material so far. Turned out to be a tiny little thing in the very last part of the process, and I'd tried the solution before on the previous parts of the process. As always in life, if you just don't give up, you're bound to succeed sooner or later.8ReplyShareReportSaveFollowlevel 1 · 4 yr. agoI mean, what's your background? How much math do you have, and when is the last time you actually used it? Do you program often?The importance of these factors is impossible to overstate when it comes to learning ML.6ReplyShareReportSaveFollowlevel 2Op · 4 yr. agoI am currently doing my masters degree in finance and do know python to some extent but I think not at a level where I can start with this book. I think I'll just take a step back and start with "Data Science from Scratch"1ReplyShareReportSaveFollowlevel 1 · 4 yr. agoI just glanced at the first chapter of that book. The author basically tries to fit in a few pages an undergrad degree in stats followed by a masters in machine learning. And that's done with no math, using simple language. I think the author was over-ambitious.Some people can be good software engineer and not be able to deal with math and stats. Machine learning is not for everyone. I don't know if that's your case, but not following the first chapter of that book does not mean you can't do it. As others said, really learning stats and machine learning is a long process.It seems some people hope that as the field matures, the tools will get easier to use and any software engineer could use machine learning with minimal training. I don't know if that will ever happen. But I guess it's already possible for people with not much understanding of deep learning to do fairly well in Kaggle competitions by just modifying existing scripts.3ReplyShareReportSaveFollowlevel 1 · 4 yr. agoJeremy Howard's MOOCs are a great hands on way to learn about Machine Learning from a practical standpoint. The videos are very understandable and well taught.3ReplyShareReportSaveFollowlevel 1 · 4 yr. ago · edited 4 yr. agoMy idea of a minimal-brain approach is: if you can download a github project that interests you, install python [2|3], [tensorflow|pytorch|..], and whatever else required to fire up the project, once you see some numbers and tinker blindly, it could keep you motivated to learn more. Even simpler, work through the keras examples or the like.I understood enough to know that I'd never understand enough, and wasted a year studying stuff I forgot too soon before just jumping in the pool and trying something.I wonder what proportion of time practicing ML folk at all levels spend just wondering whether to double or quadruple some parameter or the like. :-)3ReplyShareReportSaveFollowlevel 1 · 4 yr. agoWhile I haven't seen this particular textbook, I think video lectures are a much better way to get started with a new subject in general. I'd give some MOOC course a try before moving on to learning from textbooks.2ReplyShareReportSaveFollowlevel 1 · 4 yr. agoDo you already have a decent background in programming and computer science? Some people occasionally post here that they're struggling, and it's largely because they are learning both programming/CS concepts AND Machine Learning concepts all at once.2ReplyShareReportSaveFollowlevel 1 · 4 yr. agoNo, not at all. I went from zero to Machine Learning in a month. Granted, like others are saying, you have to learn the background. So when I say I learned it in a month, please know that I was spending 60-80 hours a week catching up on the math and programming components.But I was able to build an artificial neural network, a convolutional neural network, a recurrent neural network, and a generative adversarial network.It. Is. Possible.There are literally hundreds of tutorials with hundreds of learning styles and if I, a college dropout with a customer service and comedy background can do it, literally anyone can.2ReplyShareReportSaveFollowlevel 2 · 4 yr. agothe "comedy background" part, gave me a chuckle. You're a comedienne harry!2ReplyShareReportSaveFollowContinue this thread level 1 · 4 yr. agoHands-On Machine Learning with Scikit-Learn & TensorflowHey, I just finished part 1 of this book and I'd like to say that this is an unearthly difficult way to start reading about Machine Learning. For the record, I've finished Andrew Ng's course and have been reading Python for Data Analysis as prep for this book, and I'm still lost at times.Personally I'd start with Andrew Ng's online course if you haven't already, it's great for beginners to learn some of the machinery going on in the problems without overwhelming the student. I also don't know what your knowledge of python is, but I'd also recommend Python For Data Analysis because it goes in-depth with how to use Pandas, Numpy and Matplotlib if you're not comfortable with that.Also, I would say feel free to read and come back to chapters 2/3 if you're feeling a little lost; in my opinion these were wayyyyy harder than chapters 4-9.2ReplyShareReportSaveFollowlevel 2 · 4 yr. agochapters 2/3 of Python for Data Analysis? or Hands on Machine Learning with Scikit learn & tensor flow?1ReplyShareReportSaveFollowContinue this thread level 2Op · 3 yr. agoI just wanted to say thank for recommending Andrew Ng's course. That course is golden1ReplyShareReportSaveFollowlevel 1 · 4 yr. ago · edited 4 yr. agoI have felt the same as you. That I must be too stupid (and i still feel like that). I like that comment about learning German. And while i may be actually stupid, i am bullheaded and determined. so i've given myself till the end of this year... and if not.. then next year haha.I have the PacKt book "Machine Learning for Developers" by Rodolfo Bonnin, Oreilly's Hands-on Machine Learning with Scikit-learn & tensor Flow by Aurelien Geron, and Deep Learning with Python by Francois Chollet. I also have Sebastian Raschka's Python Machine learning.I tried to read the Bonnin book first cause its the shortest, but it felt so over my head that i blamed the author, thinking he was incoherent, ESL, or just not thorough. But after putting the book down half way, and coming back to it a month or two later, and taking my time to read, and reread each chapter, each paragraph, and really thinking about it. I found the book actually was very concise.I realized that i needed to bone up on the math and statistics concepts like, slopes, and gradients and vector math, linear algerbra, so i would stop whenever one of those concepts came up and watch a video just on that. I made it through the book and realized it was really a very cursory book as it was a practical book just "for developers"then i read the first part of the Chollet book, and it was REALLY well written and coherent. I realized that the author makes a huge difference in writing style (DUH, told you i was stupid). But i wanted to go back and really understand the foundations before moving on to deep learning.So then i started reading the Geron book, and that book is similarly written in a very clear style and i feel confident ( i still have to reread chapters and paragraphs a few times each) that I understand everything im reading intuitively.I went back to try to read the Raschka book and it is terribly dry, and i just can't follow it.Try to follow along with small made up examples to make sure you get the intuition. Im also trying to learn Python while i am learning ML, and that makes it a double challenge. Try explaining it to your SO , a captive audience, your dog , talking it outloud helps your grasp.but like eveyrone says, just stay determined. be slow and stupid, but dont give up.e: oh right, i did the Andrew Ng course, and made it to the end. but i think i would have been better served reading the books above first.2ReplyShareReportSaveFollowlevel 1 · 4 yr. agoI think anyone can understand and use basic machine learning regardless of background. Some simple algorithms like nearest neighbor you could teach to kids. So I'm sure you could become a layman in the topic.Complete understanding of the more advanced areas (neural networks+ variants for one) will require decent mathematical grounding though. That's likely where you are struggling. Also tensorflow isn't the most user friendly language if you are new to coding.1ReplyShareReportSaveFollowr/learnmachinelearningA subreddit dedicated to learning machine learning255kMembers139OnlineCreated Feb 23, 2016JoinTop posts may 17th 2018Top posts of may, 2018Top posts 2018helpReddit coinsReddit premiumReddit giftsaboutcareerspressadvertiseblogTermsContent policyPrivacy policyMod policyReddit Inc © 2022 . All rights reservedBack to Top
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                                (
                                    [0] => Two Months later?
                                    [1] => Where should I get started?
                                    [2] => I mean, seriously?
                                    [3] => You really need to know this in order to operate a computer?
                                    [4] => Reply Sean O'Connor April 23, 2018 at 2:41 pm # There is some sort of discussion about similarity alignment and also the quirky topic of feedback alignment here: https://discourse.numenta.org/t/similarity-alignment-a-missing-link-between-structure-function-and-algorithms/3683 I was also practicing html5 and I did a WHT toy example: http://md2020.eu5.org/wht1.html Reply Jason Brownlee April 23, 2018 at 2:55 pm # Great, how does this relate to the post exactly Sean?
                                    [5] => Reply Vinícius Gonçalves de Oliveira April 26, 2018 at 7:41 am # Man, how can I post a gift for you from Brazil, due the insights contained in these texts?
                                    [6] => How was the skill of your VAE on your time series problem?
                                    [7] => One question which I would like to ask is like you mentioned about a child learning how to read, a person learning how to drive in a top-down fashion, we also train machines in a similar fashion right?
                                    [8] => I’d get challenged to write a particular program, but then I’d think, “What’s the point?
                                    [9] => When I began hearing about the success of Machine Learning algorithms in other fields of science, such as medicine, it seriously caught my attention… I’ve been familiar with R for many years.
                                    [10] => What I love the most is your approach to ML.
                                    [11] => Reply Navneet December 8, 2019 at 1:47 pm # >>Are you following a top-down type approach but are riddled with guilt, math envy, and insecurities?
                                    [12] => When I went through those words, I was like is he in the same room as me!?
                                    [13] => Now that I have very basic knowledge of Python and ML, how can I get involved in small projects?
                                    [14] => Where can I find them and join as a part of team or as an individual project?
                                )

                            [body] => Why Machine Learning Does Not Have to Be So Hard By Jason Brownlee on March 28, 2018 in Start Machine Learning Tweet Tweet Share Share Last Updated on December 30, 2020 Technical topics like mathematics, physics, and even computer science are taught using a bottom-up approach. This approach involves laying out the topics in an area of study in a logical way with a natural progression in complexity and capability. The problem is, humans are not robots executing a learning program. We require motivation, excitement, and most importantly, a connection of the topic to tangible results. Useful skills we use every day like reading, driving, and programming were not learned this way and were in fact learned using an inverted top-down approach. This top-down approach can be used to learn technical subjects directly such as machine learning, which can make you a lot more productive a lot sooner, and be a lot of fun. In this post, you will discover the concrete difference between the top-down and bottom-up approaches to learning technical material and why this is the approach that practitioners should use to learn machine learning and even related mathematics. After reading this post, you will know: The bottom-up approach used in universities to teach technical subjects and the problems with it. How people learn to read, drive, and program in a top-down manner and how the top-down approach works. The frame of machine learning and even mathematics using the top-down approach to learning and how to start to make rapid progress as a practitioner. Let’s get started. You’re Doing it Wrong. Why Machine Learning Does Not Have to Be So HardPhoto by popofatticus, some rights reserved. Overview. This is an important blog post, because I think it can really help to shake you out of the bottom-up, university-style way of learning machine learning. This post is divided into seven parts; they are: Bottom-Up Learning Learning to Read Learning to Drive Learning to Code Top-Down Learning Learn Machine Learning Learning Mathematics Bottom-Up Learning. Take a field of study, such as mathematics. There is a logical way to lay out the topics in mathematics that build on each other and lead through a natural progression in skills, capability, and understanding. The problem is, this logical progression might only make sense to those who are already on the other side and can intuit the relationships between the topics. Most of school is built around this bottom-up natural progression through material. A host of technical and scientific fields of study are taught this way. Think back to high-school or undergraduate studies and the fundamental fields you may have worked through: examples such as: Mathematics, as mentioned. Biology. Chemistry. Physics. Computer Science. Think about how the material was laid out, week-by-week, semester-by-semester, year-by-year. Bottom-up, logical progression. The problem is, the logical progression through the material may not be the best way to learn the material in order to be productive. We are not robots executing a learning program. We are emotional humans that need motivation, interest, attention, encouragement, and results. You can learn technical subjects from the bottom-up, and a small percentage of people do prefer things this way, but it is not the only way. Now, if you have completed a technical subject, think back to how to you actually learned it. I bet it was not bottom-up. Learning to Read. Think back; how did you learn to read? My son is starting to read. Without thinking too much, here are the general techniques he’s using (really the school and us as parents): Start by being read to in order to generate interest and show benefits. Get the alphabet down and making the right sounds. Memorize the most frequent words, their sounds, and how to spell them. Learn the “spell-out-the-word” heuristic to deal with unknown words. Read through books with supervision. Read through books without supervision. It is important that he continually knows why reading is important, connected to very tangible things he wants to do, like: Read captions on TV shows. Read stories on topics he loves, like Star Wars. Read signs and menus when we are out and about. So on… It is also important that he gets results that he can track and in which he can see improvement. Larger vocabulary. Smoother reading style Books of increasing complexity. Here’s how he did not learn to read: Definitions of word types (verbs, nouns, adverbs, etc.) Rules of grammar. Rules of punctuation. Theory of human languages. Learning to Drive. Do you drive? It’s cool if you don’t, but most adults do out of necessity. Society and city design is built around personal mobility. How did you learn to drive? I remember some written tests and maybe a test on a computer. I have no memory of studying for them, though I very likely did. Here’s what I do remember. I remember hiring a driving instructor and doing driving lessons. Every single lesson was practical, in the car, practicing the skill I was required to master, driving the vehicle in traffic. Here’s what I did not study or discuss with my driving instructor: The history of the automobile. The theory of combustion engines. The common mechanical faults in cars. The electrical system of the car. The theory of traffic flows. To this day, I still manage to drive safely without any knowledge on these topics. In fact, I never expect to learn these topics. I have zero need or interest and they will not help me realize the thing I want and need, which is safe and easy personal mobility. If the car breaks, I’ll call an expert. Learning to Code. I started programming without any idea of what coding or software engineering meant. At home, I messed around with commands in Basic. I messed around with commands in Excel. I modified computer games. And so on. It was fun. When I started to learn programming and software engineering, it was in university and it was bottom up. We started with: Language theory Data types Control flow structures Data structures etc. When we did get to write code, it was on the command line and plagued with compiler problems, path problems, and a whole host of problems unrelated to actually learning programming. I hated programming. Flash-forward a few years. Somehow, I eventually starting working as a professional software engineer on some complex systems that were valued by their users. I was really good at it and I loved it. Eventually, I did a course that showed how to create graphical user interfaces. And another that showed how to get computers to talk to each other using socket programming. And another on how to get multiple things to run at the same time using threads. I connected the boring stuff with the thing I really liked: making software that could solve problems, that others could use. I connected it to something that mattered. It was no longer abstract and esoteric. At least for me, and many developers like me, they taught it wrong. They really did. And it wasted years of time, effort, and results/outcomes that enthusiastic and time-free students like me could dedicate to something they are truly passionate about. Top-Down Learning. The bottom-up approach is not just a common way for teaching technical topics; it looks like the only way. At least until you think about how you actually learn. The designers of university courses, masters of their subject area, are trying to help. They are laying everything out to give you the logical progression through the material that they think will get you to the skills and capabilities that you require (hopefully). And as I mentioned, it can work for some people. It does not work for me, and I expect it does not work for you. In fact, very few programmers I’ve met that are really good at their craft came through computer science programs, or if they did, they learned at home, alone, hacking on side projects. An alternative is the top-down approach. Flip the conventional approach on its head. Don’t start with definitions and theory. Instead, start by connecting the subject with the results you want and show how to get results immediately. Lay out a program that focuses on practicing this process of getting results, going deeper into some areas as needed, but always in the context of the result they require. It Is Different. It is not the traditional path. Be careful not to use traditional ways of thinking or comparison if you take this path. The onus is on you. There is no system to blame. You only fail when you stop. It is iterative. Topics are revisited many times with deeper understanding. It is imperfect. Results may be poor in the beginning, but improve with practice. It requires discovery. The learner must be open to continual learning and discoverery. It requires ownership. The learner is responsible for improvement. It requires curiosity. The learner must pay attention to what interests them and follow it. It Is Dangerous. Seriously, I’ve heard “experts” say this many times, saying things like: You have to know the theory first before you can use this technique, otherwise you cannot use it properly. I agree that results will be imperfect in the beginning, but improvement and even expertise does not only have to come from theory and fundamentals. If you believe that a beginner programmer should not be pushing changes to production and deploying them, then surely you must believe that a beginner machine learning practitioner would suffer the same constraints. Skill must be demonstrated. Trust must be earned. This is true regardless of how a skill is acquired. You’re a Technician. Really!? This is another “criticism” I’ve seen leveled at this approach to learning. Exactly. We want to be technicians, using the tools in practice to help people and not be researchers.. You do not need to cover all of the same ground because you have a different learning objective. Although you can circle back and learn anything you like later once you have a context in which to integrate the abstract knowledge. Developers in industry are not computer scientists; they are engineers. They are proud technicians of the craft. Efficient, Effective, and a Fun Way to Learn. The benefits vastly outweigh the challenge of learning this way: You go straight to the thing you want and start practicing it. You have a context for connecting deeper knowledge and even theory. You can efficiently sift and filter topics based on your goals in the subject. It’s faster. It’s more fun. And, I bet it makes you much better. How could you be better? Because the subject is connected to you emotionally. You have connected it to an outcome or result that matters to you. You are invested. You have demonstrable competence. We all love things we are good at (even if we are a little color blind to how good we are), which drives motivation, enthusiasm, and passion. An enthusiastic learner will blow straight past the fundamentalist. Learn Machine Learning. So, how have you approached the subject of machine learning? Seriously, tell me your approach in the comments below. Are you taking a bottom-up university course? Are you modeling your learning on such a course? Or worse: Are you following a top-down type approach but are riddled with guilt, math envy, and insecurities? You are not alone; I see this every single day in helping beginners on this website. To connect the dots for you, I strongly encourage you to study machine learning using the top-down approach. Don’t start with precursor math. Don’t start with machine learning theory. Don’t code every algorithm from scratch. This can all come later to refine and deepen your understanding once you have connections for this abstract knowledge. Start by learning how to work through very simple predictive modeling problems using a fixed framework with free and easy-to-use open source tools. Practice on many small projects and slowly increase their complexity.  Show your work by building a public portfolio. I have written about this approach many times; see the “Further Reading” section at the end of the post for some solid posts on how to get started with the top-down approach to machine learning. “Experts” entrenched in universities will say it’s dangerous. Ignore them. World-class practitioners will tell you it’s the way they learned and continue to learn. Model them. Remember: You learned to read by practicing reading, not by studying language theory. You learned to drive by practicing driving, not by studying combustion engines. You learned to code by practicing coding, not by studying computability theory. You can learn machine learning by practicing predictive modeling, not by studying math and theory. Not only is this the way I learned and continue to practice machine learning, but it has helped tens of thousands of my students (and the many millions of readers of this blog). Learning Mathematics. Don’t stop there. A time may come when you want or need to pull back the curtain on the mathematical pillars of machine learning such as linear algebra, calculus, statistics, probability, and so on. You can use the exact same top-down approach. Pick a goal or result that matters to you, and use that as a lens, filter, or sift on the topics to study and learn to the depth you need to get that result. For example, let’s say you pick linear algebra. A goal might be to grok SVD or PCA. These are methods used in machine learning for data projection, data reduction, and feature selection type tasks. A top-down approach might be to: Implement the method in a high-level library such as scikit-learn and get a result. Implement the method in a lower-level library such as NumPy/SciPy and reproduce the result. Implement the method directly using matrices and matrix operations in NumPy or Octave. Study and explore the matrix arithmetic operations involved. Study and explore the matrix decomposition operations involved. Study methods for approximating the eigendecomposition of a matrix. And so on… The goal provides the context and you can let your curiosity define the depth of study. Painted this way, studying math is no different to studying any other topic in programming, machine learning, or other technical subjects. It’s highly productive, and it’s a lot of fun! Further Reading. This section provides more resources on the topic if you are looking to go deeper. How Do I Get Started? Machine Learning for Programmers The Machine Learning Mastery Method Summary. In this post, you discovered the concrete difference between the top-down and bottom-up approaches to learning technical material and why this is the approach that practitioners should and do use to learn machine learning and even related mathematics. Specifically, you learned: The bottom-up approach used in universities to teach technical subjects and the problems with it. How people learn to read, drive, and program in a top-down manner and how the top-down approach works. The frame of machine learning and even mathematics using the top-down approach to learning and how to start to make rapid progress as a practitioner. Do you have any questions? Ask your questions in the comments below and I will do my best to answer. Tweet Tweet Share Share More On This Topic. Machine Learning for Developers14 Different Types of Learning in Machine LearningWhy Do I Get Different Results Each Time in Machine…The Machine Learning Mastery MethodMachine Learning BooksHow to Develop Voting Ensembles With Python Basics of Mathematical Notation for Machine Learning Comparing 13 Algorithms on 165 Datasets (hint: use Gradient Boosting) Leave a Reply Click here to cancel reply.
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                            [body] => Why is machine learning so hard to explain? Making it clear can help with stakeholder buy-in by Mary Shacklett in Artificial Intelligence on December 21, 2020, 8:58 AM PST Nobody is going to invest in a technology that they don't fully get. Helping them understand how and why it works will encourage adoption. Getty Images/iStockphoto 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) It's hard to get stakeholders to buy into technology they don't understand. In the case of artificial intelligence (AI) and machine learning (ML), very few people actually get it, leaving an explainability gap for data scientists and businesses. Three years ago, the MIT Technology Review published an article about AI titled, "The Dark Secret at the Heart of AI." "No one really knows how the most advanced algorithms do what they do. That could be a problem," Will Knight wrote. "Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey… . The car didn't follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.SEE: TechRepublic Premium editorial calendar: IT policies, checklists, toolkits, and research for download (TechRepublic Premium)"Getting a car to drive this way was an impressive feat. But it's also a bit unsettling, since it isn't completely clear how the car makes its decisions…. What if one day it did something unexpected—crashed into a tree, or sat at a green light? As things stand now, it might be difficult to find out why."  Technologies that are "hidden" in AI like machine learning are difficult for anyone to explain.That's why it creates risks for companies and for CIOs and data scientists who are expected to explain how their AI operates."The fundamental explainability flaw with AI is that it uses ML, and ML is a black box," said Will Uppington, co-founder and CEO of Truera, which provides software that aids companies in operationalizing AI and ML. "That means even when models work, data scientists don't necessarily know why. This hinders data scientists from building high quality ML applications quickly and efficiently. It also becomes a problem when non-data scientists,such as business operators, regulators, or consumers, ask questions about a result."Uppington said that Model Intelligence Platforms can help address the explainability issue."This software helps data scientists and non-data scientists explain, evaluate and extract insights from models and the data used to build the models," Uppington said. "You can think of it as the equivalent of Tableau for Machine learning. This software is also the key to ensuring that models are fair and that companies can adopt them responsibly."SEE: Natural language processing: A cheat sheet (TechRepublic)For instance, if you're a bank, you must be able to explain to regulators how your lending AI software works, and how it guards against bias. Even if you don't have to deal with regulators, technologists must be able to explain to their boards, C-level executives and end business users how an AI/ML model works, and why they should trust the results.Ensuring—and maintaining—trust in what the AI says isn't just about cleaning and vetting data to ensure that it isn't biased before the AI goes live. Over time, there is bound to be "drift" from the original data and algorithms that operate against it. You have to monitor and tune for that as well. Tools can be added to AI/ML deployment, and maintenance testing that can ascertain the accuracy of AI/ML systems. With this tooling, organizations can test against a representative number of test cases to understand how the AI's underlying "black box" ML decisioning is working, and whether the results it is delivering are "true."SEE: Artificial intelligence is struggling to cope with how the world has changed (ZDNet)In one use case, Standard Chartered Bank used software to understand how the AI model it was building operated in making the lending decisions that it made. By inputting different lending profiles and criteria, Standard Charter's team could see the results that the AI engine returned, and why. They could confirm that the decisioning of the AI stayed true to what the bank expected, and that both the data and the decision-making process were unbiased. Just as importantly, those working on the project could explain the AI process to stakeholders. They had found a way to crack open AI's ML "black box.""If data scientists can't explain how their AI applications work, then business owners aren't going to approve them, business operators aren't going to be able to manage them, and end users can reject them," Uppington said. "Companies are increasingly aware of the challenge of building trust among stakeholders. It's why the data scientists [and AI] leaders in our recent survey said that 'stakeholder collaboration' was the No. 1 organizational challenge facing their company." Data, Analytics and AI Newsletter. Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays Sign up today Also see. IT leader's guide to deep learning (TechRepublic Premium)Building the bionic brain (free PDF) (TechRepublic)Hiring Kit: Autonomous Systems Engineer (TechRepublic Premium)What is AI? Everything you need to know about Artificial Intelligence (ZDNet)Artificial Intelligence: More must-read coverage (TechRepublic on Flipboard) 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: Why is machine learning so hard to explain? Making it clear can help with stakeholder buy-in By Mary Shacklett. Mary E. Shacklett is president of Transworld Data, a technology research and market development firm. Prior to founding the company, Mary was Senior Vice President of Marketing and Technology at TCCU, Inc., a financial services firm; Vice President o... | Full Bio | See all of Mary's content Related Topics: Artificial Intelligence Big Data Analytics Innovation Internet of Things 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] => Machine Learning What it is and why it matters. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Importance     Today's World      Who Uses It     How It Works Evolution of machine learning. Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning.Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.Fraud detection? One of the more obvious, important uses in our world today.   Machine Learning and Artificial Intelligence . While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides. Why is machine learning important? Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.   What's required to create good machine learning systems? Data preparation capabilities.Algorithms – basic and advanced.Automation and iterative processes.Scalability.Ensemble modeling. Did you know? In machine learning, a target is called a label.In statistics, a target is called a dependent variable.A variable in statistics is called a feature in machine learning.A transformation in statistics is called feature creation in machine learning. Machine learning in today's world. By using algorithms to build models that uncover connections, organizations can make better decisions without human intervention. Learn more about the technologies that are shaping the world we live in. Opportunities and challenges for machine learning in business . This O'Reilly white paper provides a practical guide to implementing machine-learning applications in your organization. Read white paper Expand your skill set . Get in-depth instruction and free access to SAS Software to build your machine learning skills. Courses include: 14 hours of course time, 90 days free software access in the cloud, a flexible e-learning format, with no programming skills required.  Machine learning courses Will machine learning change your organization? . This Harvard Business Review Insight Center report looks at how machine learning will change companies and the way we manage them.     Download report Applying machine learning to IoT . Machine learning can be used to achieve higher levels of efficiency, particularly when applied to the Internet of Things. This article explores the topic. Read the IoT article Who's using it? Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors. Financial services . Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud. Government . Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft. Health care . Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient's health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.  Retail . Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history.  Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, price optimization, merchandise planning, and for customer insights.    Oil and gas . Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding. Transportation . Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations. Learn More About Industries Using This Technology. Select industry Agriculture Banking Capital Markets Casinos Education Health Care Hotels Insurance Life Sciences Manufacturing Oil & Gas Public Sector Retail Small & Midsize Business Sports Analytics Travel & Transportation Telecom, Media & Technology Utilities What are some popular machine learning methods? Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning – but there are also other methods of machine learning. Here's an overview of the most popular types. Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim. Unsupervised learning is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers. Semisupervised learning is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (because unlabeled data is less expensive and takes less effort to acquire). This type of learning can be used with methods such as classification, regression and prediction. Semisupervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process. Early examples of this include identifying a person's face on a web cam. Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy. Humans can typically create one or two good models a week; machine learning can create thousands of models a week. Thomas H. Davenport, Analytics thought leader excerpt from The Wall Street Journal What are the differences between data mining, machine learning and deep learning? Although all of these methods have the same goal – to extract insights, patterns and relationships that can be used to make decisions – they have different approaches and abilities. Data Mining . Data mining can be considered a superset of many different methods to extract insights from data. It might involve traditional statistical methods and machine learning. Data mining applies methods from many different areas to identify previously unknown patterns from data. This can include statistical algorithms, machine learning, text analytics, time series analysis and other areas of analytics. Data mining also includes the study and practice of data storage and data manipulation.   Machine Learning . The main difference with machine learning is that just like statistical models, the goal is to understand the structure of the data – fit theoretical distributions to the data that are well understood. So, with statistical models there is a theory behind the model that is mathematically proven, but this requires that data meets certain strong assumptions too. Machine learning has developed based on the ability to use computers to probe the data for structure, even if we do not have a theory of what that structure looks like. The test for a machine learning model is a validation error on new data, not a theoretical test that proves a null hypothesis. Because machine learning often uses an iterative approach to learn from data, the learning can be easily automated. Passes are run through the data until a robust pattern is found. Deep learning . Deep learning combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Deep learning techniques are currently state of the art for identifying objects in images and words in sounds. Researchers are now looking to apply these successes in pattern recognition to more complex tasks such as automatic language translation, medical diagnoses and numerous other important social and business problems. How it works . To get the most value from machine learning, you have to know how to pair the best algorithms with the right tools and processes. SAS combines rich, sophisticated heritage in statistics and data mining with new architectural advances to ensure your models run as fast as possible – even in huge enterprise environments. Algorithms: SAS graphical user interfaces help you build machine learning models and implement an iterative machine learning process. You don't have to be an advanced statistician. Our comprehensive selection of machine learning algorithms can help you quickly get value from your big data and are included in many SAS products. SAS machine learning algorithms include: Neural networks  Decision trees  Random forests  Associations and sequence discovery  Gradient boosting and bagging  Support vector machines  Nearest-neighbor mapping  k-means clustering  Self-organizing maps   Local search optimization techniques (e.g., genetic algorithms)  Expectation maximization  Multivariate adaptive regression splines  Bayesian networks  Kernel density estimation  Principal component analysis  Singular value decomposition  Gaussian mixture models  Sequential covering rule building     Tools and Processes: As we know by now, it’s not just the algorithms. Ultimately, the secret to getting the most value from your big data lies in pairing the best algorithms for the task at hand with: Comprehensive data quality and management  GUIs for building models and process flows  Interactive data exploration and visualization of model results  Comparisons of different machine learning models to quickly identify the best one       Automated ensemble model evaluation to identify the best performers  Easy model deployment so you can get repeatable, reliable results quickly  An integrated, end-to-end platform for the automation of the data-to-decision process   Do you need some basic guidance on which machine learning algorithm to use for what? This blog by Hui Li, a data scientist at SAS, provides a handy cheat sheet. Read more about this topic . Public health infrastructure desperately needs modernizationPublic health agencies must flex to longitudinal health crises and acute emergencies – from natural disasters like hurricanes to events like a pandemic. To be prepared, public health infrastructure must be modernized to support connectivity, real-time data exchanges, analytics and visualization. SAS CIO: Why leaders must cultivate curiosity in 2021With the change we’re all facing this year, CIOs should be counting on curiosity to play a crucial role in how we’re going to meet the challenges that lie ahead. From the moment COVID-19 hit, our IT organization has relied on curiosity – that strong desire to explore, learn, know - to fuel the urgent changes required. And it’s curiosity that will enable us to meet the needs of the future of work post-pandemic. Five ways your organization can enhance resilience for years to comeInnovation, agility and customer-centricity frequently top the list of companies’ strategic objectives, and now the most urgent priority is resilience. Given this new urgency, it’s worth taking a close look at the underpinnings of resilience and how they could be applied in any industry. This article explores how analytics can help boost resilience and includes key elements to keep your organization resilient. Resilience in the face of unpredictabilityUnpredictability can “shatter and reshape” a society. And in these unpredictable times, it is important to remain resilient and be prepared to bounce back. This article explores what it truly means to be resilient, how to build it, and how analytics can help you act when your resilience is tested. × × ×
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                            [body] => Deep Learning vs. Machine Learning — What’s the Difference?By Michael Middleton•February 08, 2021Data ScienceChange your career today with our full-time Online Data Science courseLearn MoreKey TakeawaysDeep learning is a type of machine learning, which is a subset of artificial intelligence.Machine learning is about computers being able to think and act with less human intervention; deep learning is about computers learning to think using structures modeled on the human brain.Machine learning requires less computing power; deep learning typically needs less ongoing human intervention.Deep learning can analyze images, videos, and unstructured data in ways machine learning can’t easily do.Every industry will have career paths that involve machine and deep learning.What is artificial intelligence (AI)?Artificial Intelligence (AI) is a science devoted to making machines think and act like humans. This may sound simple, but no existing computer begins to match the complexities of human intelligence. Computers excel at applying rules and executing tasks, but sometimes a relatively straightforward ‘action’ for a person might be extremely complex for a computer.For example, carrying a tray of drinks through a crowded bar and serving them to the correct customer is something servers do every day, but it is a complex exercise in decision making and based on a high volume of data being transmitted between neurons in the human brain.Computers aren’t there yet, but machine learning and deep learning are steps towards a key element of this goal: analyzing large volumes of data and making decisions/predictions based on it with as little human intervention as possible.What is machine learning?Machine Learning is a subset of artificial intelligence focusing on a specific goal: setting computers up to be able to perform tasks without the need for explicit programming.Computers are fed structured data (in most cases) and ‘learn’ to become better at evaluating and acting on that data over time. Think of ‘structured data’ as data inputs you can put in columns and rows. You might create a category column in Excel called ‘food’, and have row entries such as ‘fruit’ or ‘meat’. This form of ‘structured’ data is very easy for computers to work with, and the benefits are obvious (It’s no coincidence that one of the most important data programming languages is called ‘structured query language’). Once programmed, a computer can take in new data indefinitely, sorting and acting on it without the need for further human intervention. Over time, the computer may be able to recognize that ‘fruit’ is a type of food even if you stop labeling your data. This ‘self-reliance’ is so fundamental to machine learning that the field breaks down into subsets based on how much ongoing human help is involved.Supervised learning & semi-supervised learning. Supervised learning is a subset of machine learning that requires the most ongoing human participation — hence the name ‘supervised’. The computer is fed training data and a model explicitly designed to ‘teach’ it how to respond to the data. Once the model is in place, more data can be fed into the computer to see how well it responds — and the programmer/data scientist can confirm accurate predictions, or can issue corrections for any incorrect responses. Picture a programmer trying to teach a computer image classification. They’d input images and task the computer to classify each image, confirming or correcting each computer output.Over time, this level of supervision helps hone the model into something that is accurately able to handle new datasets that follow the ‘learned’ patterns. But it is not efficient to keep monitoring the computer’s performance and making adjustments.In semi-supervised learning, the computer is fed a mixture of correctly labeled data and unlabeled data, and searches for patterns on its own. The labeled data serves as ‘guidance’ from the programmer, but they do not issue ongoing corrections.Unsupervised learning. Unsupervised learning takes this a step further by using unlabeled data. The computer is given the freedom to find patterns and associations as it sees fit, often generating results that might have been unapparent to a human data analyst.A common use for unsupervised learning is ‘clustering’, where the computer organizes the data into common themes and layers it identifies. Shopping/e-commerce websites routinely use this technology to decide what recommendations to make to specific users based on their past purchases.Reinforcement learning. In supervised and unsupervised learning, there is no ‘consequence’ to the computer if it fails to properly understand or categorize data. But what if, like a child at school, it received positive feedback when it did the right thing, and negative feedback when it did the wrong thing? The computer would presumably begin to figure out how to get specific tasks job done through trial-and-error, knowing it’s on the right track when it receives a reward (for example, a score) that reinforces its ‘good behavior’. This type of reinforced learning is critical to helping machines master complex tasks that come with large, highly flexible, and unpredictable datasets. This opens the door to computers that are trying to achieve a goal: perform surgery, drive a car, scan luggage for dangerous objects, etc. What is machine learning used for today?You might be surprised to find that you interact with machine learning tools every day. Google uses it to filter spam, malware, and attempted phishing emails out of your inbox. Your bank and credit card use it to generate warnings about suspicious transactions on your accounts. When you talk to Siri and Alexa, machine learning drives the voice and speech recognition platforms at work. And when your doctor sends you to a specialist, machine learning may be helping them scan X-rays and blood test results for anomalies like cancer. As the applications continue to grow, people are turning to machine learning to handle increasingly more complex types of data. There is a strong demand for computers that can handle unstructured data, like images or video. And this is where deep learning enters the picture.What is deep learning?Machine learning is about computers being able to perform tasks without being explicitly programmed… but the computers still think and act like machines. Their ability to perform some complex tasks — gathering data from an image or video, for example — still falls far short of what humans are capable of.Deep learning models introduce an extremely sophisticated approach to machine learning and are set to tackle these challenges because they've been specifically modeled after the human brain. Complex, multi-layered “deep neural networks” are built to allow data to be passed between nodes (like neurons) in highly connected ways. The result is a non-linear transformation of the data that is increasingly abstract.While it takes tremendous volumes of data to ‘feed and build’ such a system, it can begin to generate immediate results, and there is relatively little need for human intervention once the programs are in place.Types of deep learning algorithms. A growing number of deep learning algorithms make these new goals reachable. We’ll cover two here just to illustrate some of the ways that data scientists and engineers are going about applying deep learning in the field.Convolutional Neural NetworksConvolutional neural networks are specially built algorithms designed to work with images. The ‘convolution’ in the title is the process that applies a weight-based filter across every element of an image, helping the computer to understand and react to elements within the picture itself. This can be helpful when you need to scan a high volume of images for a specific item or feature; for example, images of the ocean floor for signs of a shipwreck, or a photo of a crowd for a single person’s face. This science of computer image/video analysis and comprehension is called ‘computer vision’, and represents a high-growth area in the industry over the past 10 years.Recurrent Neural NetworksRecurrent neural networks, meanwhile, introduce a key element into machine learning that is absent in simpler algorithms: memory. The computer is able to keep past data points and decisions ‘in mind’, and consider them when reviewing current data – introducing the power of context.This has made recurrent neural networks a major focus for natural language processing work. Like with a human, the computer will do a better job understanding a section of text if it has access to the tone and content that came before it. Likewise, driving directions can be more accurate if the computer ‘remembers’ that everyone following a recommended route on a Saturday night takes twice as long to get where they are going.5 key differences between machine learning and deep learning. While there are many differences between these two subsets of artificial intelligence, here are five of the most important:1. Human Intervention. Machine learning requires more ongoing human intervention to get results. Deep learning is more complex to set up but requires minimal intervention thereafter.2. Hardware. Machine learning programs tend to be less complex than deep learning algorithms and can often run on conventional computers, but deep learning systems require far more powerful hardware and resources. This demand for power has driven has meant increased use of graphical processing units. GPUs are useful for their high bandwidth memory and ability to hide latency (delays) in memory transfer due to thread parallelism (the ability of many operations to run efficiently at the same time.)3. Time. Machine learning systems can be set up and operate quickly but may be limited in the power of their results. Deep learning systems take more time to set up but can generate results instantaneously (although the quality is likely to improve over time as more data becomes available).4. Approach. Machine learning tends to require structured data and uses traditional algorithms like linear regression. Deep learning employs neural networks and is built to accommodate large volumes of unstructured data.5. Applications. Machine learning is already in use in your email inbox, bank, and doctor’s office. Deep learning technology enables more complex and autonomous programs, like self-driving cars or robots that perform advanced surgery.The future of machine learning and deep learning. Machine and deep learning will affect our lives for generations to come and virtually every industry will be transformed by their capabilities. Dangerous jobs like space travel or work in harsh environments might be entirely replaced with machine involvement.At the same time, people will turn to artificial intelligence to deliver rich new entertainment experiences that seem like the stuff of science fiction.Careers in machine learning and deep learning. It will take the continued efforts of talented individuals to help machine and deep learning achieve their best results. While every field will have its own special needs in this space, there are some key career paths that already enjoy competitive hiring environments.Data Scientists. Data Scientists work to compose the models and algorithms needed to pursue their industry’s goals. They also oversee the processing and analysis of data generated by the computers. This fast-growing career combines a need for coding expertise (Python, Java, etc.) with a strong understanding of the business and strategic goals of a company or industry. Average Glassdoor salary: $113k/yearAverage ZipRecruiter salary: $120k/yearMachine Learning Engineers. Machine Learning Engineers implement the data scientists’ models and integrate them into the complex data and technological ecosystems of the firm. They are also at the helm for the implementation/programming of automated controls or robots that take actions based on incoming data. This is critical work — the massive volume of data and computer processing power requires a high level of expertise and efficiency to be both cost- and resource-effective.Average Glassdoor salary: $114k/yearAverage ZipRecruiter salary: $131k/yearComputer Vision Specialist. Computer Vision Specialists help computers make sense of 2D or 3D images and are critical to many practical applications of deep learning, such as the augmented and virtual reality spaces. This is just an example of a specific career that exists within the machine learning ecosystem; every industry will have its own specialists to help unite the powers of artificial intelligence with industry goals and technologies.Average Glassdoor salary: $114k/yearAverage ZipRecruiter salary: $96k/yearIf you’re curious about pursuing a data science career, our data science course covers entire modules devoted to machine learning, deep learning, and natural language processing. We offer this course both in person and as an online course.All it takes is some math know-how and familiarity with basic data analysis. Here are some tips for getting accepted into our data science course.Michael Middleton. WriterRead More Data Science ArticlesSince we opened our doors in 2012, thousands of students have joined Flatiron School to launch new careers in tech.Find the perfect course for you across our in-person and online programs designed to power your career change.Explore Our CoursesConnect with students and staff at meetups, lectures, and demos – on campus and online.Join the CommunityHave a question about our programs? Our admissions team is here to help.Schedule a Chat
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                            [body] => Machine learning From Wikipedia, the free encyclopedia Jump to navigation Jump to search Study of algorithms that improve automatically through experience For the journal, see Machine Learning (journal). "Statistical learning" redirects here. For statistical learning in linguistics, see statistical learning in language acquisition. Part of a series onMachine learningand data mining Problems Classification Clustering Regression Anomaly detection Data Cleaning AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning(classification • regression) Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM) Clustering BIRCH CURE Hierarchical k-means Expectation–maximization (EM) DBSCAN OPTICS Mean shift Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection k-NN Local outlier factor Artificial neural network Autoencoder Cognitive computing Deep learning DeepDream Multilayer perceptron RNN LSTM GRU ESN Restricted Boltzmann machine GAN SOM Convolutional neural network U-Net Transformer Vision Spiking neural network Memtransistor Electrochemical RAM (ECRAM) Reinforcement learning Q-learning SARSA Temporal difference (TD) Theory Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory Machine-learning venues NeurIPS ICML ML JMLR ArXiv:cs.LG Related articles Glossary of artificial intelligence List of datasets for machine-learning research Outline of machine learning vte Part of a series onArtificial intelligence Major goals Artificial general intelligence Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics Approaches Symbolic Deep learning Bayesian networks Evolutionary algorithms Philosophy Chinese room Friendly AI Control problem/Takeover Ethics Existential risk Turing test History Timeline Progress AI winter Technology Applications Projects Programming languages Glossary Glossary vte Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.[1] It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.[2] Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[3] A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.[5][6] Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain.[7][8] In its application across business problems, machine learning is also referred to as predictive analytics. Contents. 1 Overview 2 History and relationships to other fields 2.1 Artificial intelligence 2.2 Data mining 2.3 Optimization 2.4 Generalization 2.5 Statistics 3 Theory 4 Approaches 4.1 Supervised learning 4.2 Unsupervised learning 4.3 Semi-supervised learning 4.4 Reinforcement learning 4.5 Dimensionality reduction 4.6 Other types 4.6.1 Self learning 4.6.2 Feature learning 4.6.3 Sparse dictionary learning 4.6.4 Anomaly detection 4.6.5 Robot learning 4.6.6 Association rules 4.7 Models 4.7.1 Artificial neural networks 4.7.2 Decision trees 4.7.3 Support-vector machines 4.7.4 Regression analysis 4.7.5 Bayesian networks 4.7.6 Genetic algorithms 4.8 Training models 4.8.1 Federated learning 5 Applications 6 Limitations 6.1 Bias 6.2 Overfitting 6.3 Other limitations 7 Model assessments 8 Ethics 9 Hardware 9.1 Neuromorphic/Physical Neural Networks 10 Software 10.1 Free and open-source software 10.2 Proprietary software with free and open-source editions 10.3 Proprietary software 11 Journals 12 Conferences 13 See also 14 References 15 Sources 16 Further reading 17 External links Overview[edit]. Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist".[9] Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.[10] The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.[10] History and relationships to other fields[edit]. See also: Timeline of machine learning The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence.[11][12] Also the synonym self-teaching computers was used in this time period.[13][14] A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.[15] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.[16] In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.[17] Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."[18] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".[19] Modern day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. Where as, a machine learning algorithm for stock trading may inform the trader of future potential predictions.[20] Artificial intelligence[edit]. Machine learning as subfield of AI[21] Part of machine learning as subfield of AI or part of AI as subfield of machine learning[22] As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.[23] Probabilistic reasoning was also employed, especially in automated medical diagnosis.[24]: 488  However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.[24]: 488  By 1980, expert systems had come to dominate AI, and statistics was out of favor.[25] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[24]: 708–710, 755  Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.[24]: 25  Machine learning (ML), reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.[25] The difference between ML and AI is frequently misunderstood. ML learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals.[26] As of 2020, many sources continue to assert that ML remains a subfield of AI.[27][28][25] Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI.[4][29][30] Data mining[edit]. Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. Optimization[edit]. Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).[31] Generalization[edit]. The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms. Statistics[edit]. Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.[32] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[33] He also suggested the term data science as a placeholder to call the overall field.[33] Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[27] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[28] Theory[edit]. Main articles: Computational learning theory and Statistical learning theory A core objective of a learner is to generalize from its experience.[4][29] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error. For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.[30] In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. Approaches[edit]. Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.[4] Supervised learning[edit]. Main article: Supervised learning A support-vector machine is a supervised learning model that divides the data into regions separated by a linear boundary. Here, the linear boundary divides the black circles from the white. Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.[34] The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.[35] An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.[18] Types of supervised learning algorithms include active learning, classification and regression.[26] Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification. Unsupervised learning[edit]. Main article: Unsupervised learningSee also: Cluster analysis Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function.[36] Though unsupervised learning encompasses other domains involving summarizing and explaining data features. Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity. Semi-supervised learning[edit]. Main article: Semi-supervised learning Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.[37] Reinforcement learning[edit]. Main article: Reinforcement learning Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcement learning algorithms use dynamic programming techniques.[38] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. Dimensionality reduction[edit]. Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.[39] In other words, it is a process of reducing the dimension of the feature set, also called "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data.[40] The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization. Other types[edit]. Other approaches have been developed which don't fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example topic modeling, meta learning.[41] As of 2020, deep learning has become the dominant approach for much ongoing work in the field of machine learning.[10] Self learning[edit]. Self-learning as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA).[42] It is a learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.[43] The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: In situation s perform an action a; Receive consequence situation s’; Compute emotion of being in consequence situation v(s’); Update crossbar memory w’(a,s) = w(a,s) + v(s’). It is a system with only one input, situation s, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations.[44] Feature learning[edit]. Main article: Feature learning Several learning algorithms aim at discovering better representations of the inputs provided during training.[45] Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task. Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization[46] and various forms of clustering.[47][48][49] Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors.[50] Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.[51] Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. Sparse dictionary learning[edit]. Main article: Sparse dictionary learning Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately.[52] A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.[53] Anomaly detection[edit]. Main article: Anomaly detection In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.[54] Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.[55] In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.[56] Three broad categories of anomaly detection techniques exist.[57] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model. Robot learning[edit]. Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning,[58][59] and finally meta-learning (e.g. MAML). Association rules[edit]. Main article: Association rule learningSee also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule { o n i o n s , p o t a t o e s } ⇒ { b u r g e r } {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models[edit]. Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks[edit]. Main article: Artificial neural networkSee also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees[edit]. Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support-vector machines[edit]. Main article: Support-vector machine Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis[edit]. Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks[edit]. Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms[edit]. Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models[edit]. Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Trained models derived from biased data can result in skewed or undesired predictions. Algorithmic bias is a potential result from data not fully prepared for training. Federated learning[edit]. Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications[edit]. There are many applications for machine learning, including: Agriculture Anatomy Adaptive website Affective computing Astronomy Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Knowledge graph embedding Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time-series forecasting User behavior analytics Behaviorism In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19.[82] Machine learning is recently applied to predict the green behavior of human-being.[83] Recently, machine learning technology is also applied to optimise smartphone's performance and thermal behaviour based on the user's interaction with the phone.[84][85] Limitations[edit]. Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[86][87][88] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[89] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[90] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[91][92] Machine learning has been used as a strategy to update the evidence related to systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves.[93] Bias[edit]. Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society.[94] Language models learned from data have been shown to contain human-like biases.[95][96] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[97][98] In 2015, Google photos would often tag black people as gorillas,[99] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[100] Similar issues with recognizing non-white people have been found in many other systems.[101] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[102] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[103] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[104] Overfitting[edit]. Main article: Overfitting The blue line could be an example of overfitting a linear function due to random noise. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.[9] Other limitations[edit]. Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.[105] A real-world example is that, unlike humans, current image classifiers often don't primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.[106][107] Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification. Model assessments[edit]. Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[108] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[109] Ethics[edit]. See also: AI control problem and Toronto Declaration Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[110] For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names.[94] Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants.[111][112] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on objectivity and logical reasoning.[113] Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[114][115] Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines.[116] This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.[117] Hardware[edit]. Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[118] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[119] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[120][121] Neuromorphic/Physical Neural Networks[edit]. A physical neural network or Neuromorphic computer is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.[122][123] Software[edit]. Software suites containing a variety of machine learning algorithms include the following: Free and open-source software[edit]. Caffe Microsoft Cognitive Toolkit Deeplearning4j DeepSpeed ELKI Infer.NET Keras LightGBM Mahout Mallet ML.NET mlpack MXNet Neural Lab OpenNN Orange pandas (software) ROOT (TMVA with ROOT) scikit-learn Shogun Spark MLlib SystemML TensorFlow Torch / PyTorch Weka / MOA XGBoost Yooreeka Proprietary software with free and open-source editions[edit]. KNIME RapidMiner Proprietary software[edit]. Amazon Machine Learning Angoss KnowledgeSTUDIO Azure Machine Learning Ayasdi IBM Watson Studio Google Prediction API IBM SPSS Modeler KXEN Modeler LIONsolver Mathematica MATLAB Neural Designer NeuroSolutions Oracle Data Mining Oracle AI Platform Cloud Service PolyAnalyst RCASE SAS Enterprise Miner SequenceL Splunk STATISTICA Data Miner Journals[edit]. Journal of Machine Learning Research Machine Learning Nature Machine Intelligence Neural Computation Conferences[edit]. Association for Computational Linguistics (ACL) European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) International Conference on Machine Learning (ICML) International Conference on Learning Representations (ICLR) International Conference on Intelligent Robots and Systems (IROS) Conference on Knowledge Discovery and Data Mining (KDD) Conference on Neural Information Processing Systems (NeurIPS) See also[edit]. 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ISSN 0028-4793. PMC 5962261. PMID 29539284. ^ Char, D. S.; Shah, N. H.; Magnus, D. (2018). "Implementing Machine Learning in Health Care—Addressing Ethical Challenges". New England Journal of Medicine. 378 (11): 981–983. doi:10.1056/nejmp1714229. PMC 5962261. PMID 29539284. ^ Research, AI (23 October 2015). "Deep Neural Networks for Acoustic Modeling in Speech Recognition". airesearch.com. Retrieved 23 October 2015. ^ "GPUs Continue to Dominate the AI Accelerator Market for Now". InformationWeek. December 2019. Retrieved 11 June 2020. ^ Ray, Tiernan (2019). "AI is changing the entire nature of compute". ZDNet. Retrieved 11 June 2020. ^ "AI and Compute". OpenAI. 16 May 2018. Retrieved 11 June 2020. ^ "Cornell & NTT's Physical Neural Networks: A "Radical Alternative for Implementing Deep Neural Networks" That Enables Arbitrary Physical Systems Training | Synced". 27 May 2021. ^ "Nano-spaghetti to solve neural network power consumption". Sources[edit]. Domingos, Pedro (September 22, 2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0465065707. Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2. Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-19-510270-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Further reading[edit]. Nils J. Nilsson, Introduction to Machine Learning. Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0-387-95284-5. Pedro Domingos (September 2015), The Master Algorithm, Basic Books, ISBN 978-0-465-06570-7 Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., ISBN 978-0-12-374856-0. Ethem Alpaydin (2004). Introduction to Machine Learning, MIT Press, ISBN 978-0-262-01243-0. David J. C. MacKay. Information Theory, Inference, and Learning Algorithms Cambridge: Cambridge University Press, 2003. ISBN 0-521-64298-1 Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3. Christopher Bishop (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2. Stuart Russell & Peter Norvig, (2009). Artificial Intelligence – A Modern Approach. Pearson, ISBN 9789332543515. Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957. Ray Solomonoff, An Inductive Inference Machine A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI. Kevin P. Murphy (2021). Probabilistic Machine Learning: An Introduction, MIT Press. External links[edit]. Wikimedia Commons has media related to Machine learning. Quotations related to Machine learning at Wikiquote International Machine Learning Society mloss is an academic database of open-source machine learning software. vteDifferentiable computingGeneral Differentiable programming Neural Turing machine Differentiable neural computer Automatic differentiation Neuromorphic engineering Cable theory Pattern recognition Computational learning theory Tensor calculus Concepts Gradient descent SGD Clustering Regression Overfitting Adversary Attention Convolution Loss functions Backpropagation Normalization Activation Softmax Sigmoid Rectifier Regularization Datasets Augmentation Programming languages Python Julia Application Machine learning Artificial neural network Deep learning Scientific computing Artificial Intelligence Hardware IPU TPU VPU Memristor SpiNNaker Software library TensorFlow PyTorch Keras Theano ImplementationAudio-visual AlexNet WaveNet Human image synthesis HWR OCR Speech synthesis Speech recognition Facial recognition AlphaFold 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                            [body] => When Machine Learning Goes Off the Rails A guide to managing the risks by Boris Babic, I. Glenn Cohen, Theodoros Evgeniou, and Sara Gerke by Boris Babic, I. Glenn Cohen, Theodoros Evgeniou, and Sara Gerke From the Magazine (January–February 2021) · Long read Gregory Reid/Gallery Stock Summary.    Products and services that rely on machine learning—computer programs that constantly absorb new data and adapt their decisions in response—don’t always make ethical or accurate choices. Sometimes they cause investment losses, for instance, or biased hiring or car accidents. And as such offerings proliferate across markets, the companies creating them face major new risks. Executives need to understand and mitigate the technology’s potential downside. Machine learning can go wrong in a number of ways. Because the systems make decisions based on probabilities, some errors are always possible. Their environments may evolve in unanticipated ways, creating disconnects between the data they were trained with and the data they’re currently fed. And their complexity can make it hard to determine whether or why they made a mistake. A key question executives must answer is whether it’s better to allow smart offerings to continuously evolve or to “lock” their algorithms and periodically update them. In addition, every offering will need to be appropriately tested before and after rollout and regularly monitored to make sure it’s performing as intended. Tweet Post Share Save Get PDF Buy Copies Print Idea in Brief. The Problem Offerings that rely on machine learning are proliferating, raising all sorts of new risks for companies that develop and use them or supply data to train them. That’s because such systems don’t always make ethical or accurate choices. The Causes First, the systems often make decisions based on probabilities. Second, their environments may evolve in an unanticipated way. Third, their complexity makes it difficult to determine whether or why they made a mistake. The Solutions Executives must decide whether to let a system continuously evolve or introduce locked versions at intervals. In addition, they should test the offering appropriately before and after it is rolled out and monitor it constantly once it’s on the market. Leer en español Ler em português What happens when machine learning—computer programs that absorb new information and then change how they make decisions—leads to investment losses, biased hiring or lending, or car accidents? Should businesses allow their smart products and services to autonomously evolve, or should they “lock” their algorithms and periodically update them? If firms choose to do the latter, when and how often should those updates happen? And how should companies evaluate and mitigate the risks posed by those and other choices? Across the business world, as machine-learning-based artificial intelligence permeates more and more offerings and processes, executives and boards must be prepared to answer such questions. In this article, which draws on our work in health care law, ethics, regulation, and machine learning, we introduce key concepts for understanding and managing the potential downside of this advanced technology. What Makes Machine Learning Risky. The big difference between machine learning and the digital technologies that preceded it is the ability to independently make increasingly complex decisions—such as which financial products to trade, how vehicles react to obstacles, and whether a patient has a disease—and continuously adapt in response to new data. But these algorithms don’t always work smoothly. They don’t always make ethical or accurate choices. There are three fundamental reasons for this. One is simply that the algorithms typically rely on the probability that someone will, say, default on a loan or have a disease. Because they make so many predictions, it’s likely that some will be wrong, just because there’s always a chance that they’ll be off. The likelihood of errors depends on a lot of factors, including the amount and quality of the data used to train the algorithms, the specific type of machine-learning method chosen (for example, deep learning, which uses complex mathematical models, versus classification trees that rely on decision rules), and whether the system uses only explainable algorithms (meaning humans can describe how they arrived at their decisions), which may not allow it to maximize accuracy. Second, the environment in which machine learning operates may itself evolve or differ from what the algorithms were developed to face. While this can happen in many ways, two of the most frequent are concept drift and covariate shift. With the former the relationship between the inputs the system uses and its outputs isn’t stable over time or may be misspecified. Consider a machine-learning algorithm for stock trading. If it has been trained using data only from a period of low market volatility and high economic growth, it may not perform well when the economy enters a recession or experiences turmoil—say, during a crisis like the Covid-19 pandemic. As the market changes, the relationship between the inputs and outputs—for example, between how leveraged a company is and its stock returns—also may change. Similar misalignment may happen with credit-scoring models at different points in the business cycle. In medicine, an example of concept drift is when a machine-learning-based diagnostic system that uses skin images as inputs in detecting skin cancers fails to make correct diagnoses because the relationship between, say, the color of someone’s skin (which may vary with race or sun exposure) and the diagnosis decision hasn’t been adequately captured. Such information often is not even available in electronic health records used to train the machine-learning model. Covariate shifts occur when the data fed into an algorithm during its use differs from the data that trained it. This can happen even if the patterns the algorithm learned are stable and there’s no concept drift. For example, a medical device company may develop its machine-learning-based system using data from large urban hospitals. But once the device is out in the market, the medical data fed into the system by care providers in rural areas may not look like the development data. The urban hospitals might have a higher concentration of patients from certain sociodemographic groups who have underlying medical conditions not commonly seen in rural hospitals. Such disparities may be discovered only when the device makes more errors while out in the market than it did during testing. Given the diversity of markets and the pace at which they’re changing, it’s becoming increasingly challenging to foresee what will happen in the environment that systems operate in, and no amount of data can capture all the nuances that occur in the real world. How should we program an autonomous car to value the lives of three elderly people against, say, the life of one middle-aged person? The third reason machine learning can make inaccurate decisions has to do with the complexity of the overall systems it’s embedded in. Consider a device used to diagnose a disease on the basis of images that doctors input—such as IDx-DR, which identifies eye disorders like diabetic retinopathy and macular edema and was the first autonomous machine-learning-based medical device authorized for use by the U.S. Food and Drug Administration. The quality of any diagnosis depends on how clear the images provided are, the specific algorithm used by the device, the data that algorithm was trained with, whether the doctor inputting the images received appropriate instruction, and so on. With so many parameters, it’s difficult to assess whether and why such a device may have made a mistake, let alone be certain about its behavior. But inaccurate decisions are not the only risks with machine learning. Let’s look now at two other categories: agency risk and moral risk. Agency Risk. The imperfections of machine learning raise another important challenge: risks stemming from things that aren’t under the control of a specific business or user. Ordinarily, it’s possible to draw on reliable evidence to reconstruct the circumstances that led to an accident. As a result, when one occurs, executives can at least get helpful estimates of the extent of their company’s potential liability. But because machine learning is typically embedded within a complex system, it will often be unclear what led to a breakdown—which party, or “agent” (for example, the algorithm developer, the system deployer, or a partner), was responsible for an error and whether there was an issue with the algorithm, with some data fed to it by the user, or with the data used to train it, which may have come from multiple third-party vendors. Environmental change and the probabilistic nature of machine learning make it even harder to attribute responsibility to a particular agent. In fact, accidents or unlawful decisions can occur even without negligence on anyone’s part—as there is simply always the possibility of an inaccurate decision. Gregory Reid/Gallery Stock Executives need to know when their companies are likely to face liability under current law, which may itself also evolve. Consider the medical context. Courts have historically viewed doctors as the final decision-makers and have therefore been hesitant to apply product liability to medical software makers. However, this may change as more black-box or autonomous systems make diagnoses and recommendations without the involvement of (or with much weaker involvement by) physicians in clinics. What will happen, for example, if a machine-learning system recommends a nonstandard treatment for a patient (like a much higher drug dosage than usual) and regulation evolves in such a way that the doctor would most likely be held liable for any harm only if he or she did not follow the system’s recommendation? Such regulatory changes may shift liability risks from doctors to the developers of the machine-learning-enabled medical devices, the data providers involved in developing the algorithms, or the companies involved in installing and deploying the algorithms. Moral Risk. Products and services that make decisions autonomously will also need to resolve ethical dilemmas—a requirement that raises additional risks and regulatory and product development challenges. Scholars have now begun to frame these challenges as problems of responsible algorithm design. They include the puzzle of how to automate moral reasoning. Should Tesla, for example, program its cars to think in utilitarian cost-benefit terms or Kantian ones, where certain values cannot be traded off regardless of benefits? Even if the answer is utilitarian, quantification is extremely difficult: How should we program a car to value the lives of three elderly people against, say, the life of one middle-aged person? How should businesses balance trade-offs among, say, privacy, fairness, accuracy, and security? Can all those kinds of risks be avoided? Moral risks also include biases related to demographic groups. For example, facial-recognition algorithms have a difficult time identifying people of color; skin-lesion-classification systems appear to have unequal accuracy across race; recidivism-prediction instruments give Blacks and Hispanics falsely high ratings, and credit-scoring systems give them unjustly low ones. With many widespread commercial uses, machine-learning systems may be deemed unfair to a certain group on some dimensions. The problem is compounded by the multiple and possibly mutually incompatible ways to define fairness and encode it in algorithms. A lending algorithm can be calibrated—meaning that its decisions are independent of group identity after controlling for risk level—while still disproportionately denying loans to creditworthy minorities. As a result, a company can find itself in a “damned if you do, damned if you don’t” situation. If it uses algorithms to decide who receives a loan, it may have difficulty avoiding charges that it’s discriminating against some groups according to one of the definitions of fairness. Different cultures may also accept different definitions and ethical trade-offs—a problem for products with global markets. A February 2020 European Commission white paper on AI points to these challenges: It calls for the development of AI with “European values,” but will such AI be easily exported to regions with different values? Executives need to think of machine learning as a living entity, not an inanimate technology. Finally, all these problems can also be caused by model instability. This is a situation where inputs that are close to one another lead to decisions that are far apart. Unstable algorithms are likely to treat very similar people very differently—and possibly unfairly. All these considerations, of course, don’t mean that we should avoid machine learning altogether. Instead, executives need to embrace the opportunities it creates while making sure they properly address the risks. To Lock or Not to Lock? If leaders decide to employ machine learning, a key next question is: Should the company allow it to continuously evolve or instead introduce only tested and locked versions at intervals? Would the latter choice mitigate the risks just described? This problem is familiar to the medical world. The FDA has so far typically approved only “software as a medical device” (software that can perform its medical functions without hardware) whose algorithms are locked. The reasoning: The agency has not wanted to permit the use of devices whose diagnostic procedures or treatment pathways keep changing in ways it doesn’t understand. But as the FDA and other regulators are now realizing, locking the algorithms may be just as risky, because it doesn’t necessarily remove the following dangers: Inaccurate decisions. Locking doesn’t alter the fact that machine-learning algorithms typically base decisions on estimated probabilities. Moreover, while the input of more data usually leads to better performance, it doesn’t always, and the amount of improvement can vary; improvements in unlocked algorithms may be greater or smaller for different systems and with different volumes of data. Though it’s difficult to understand how the accuracy (or inaccuracy) of decisions may change when an algorithm is unlocked, it’s important to try. Environmental changes. It also matters whether and how the environment in which the system makes decisions is evolving. For example, car autopilots operate in environments that are constantly altered by the behavior of other drivers. Pricing, credit scoring, and trading systems may face a shifting market regime whenever the business cycle enters a new phase. The challenge is ensuring that the machine-learning system and the environment coevolve in a way that lets the system make appropriate decisions. Agency risks. Locking an algorithm doesn’t eliminate the complexity of the system in which it’s embedded. For example, errors caused by using inferior data from third-party vendors to train the algorithm or by differences in skills across users can still occur. Liability can still be challenging to assign across data providers, algorithm developers, deployers, and users Moral risks. A locked system may preserve imperfections or biases unknown to its creators. When analyzing mammograms for signs of breast cancer, a locked algorithm would be unable to learn from new subpopulations to which it is applied. Since average breast density can differ by race, this could lead to misdiagnoses if the system screens people from a demographic group that was underrepresented in the training data. Similarly, a credit-scoring algorithm trained on a socioeconomically segregated subset of the population can discriminate against certain borrowers in much the same way that the illegal practice of redlining does. We want algorithms to correct for such problems as soon as possible by updating themselves as they “observe” more data from subpopulations that may not have been well represented or even identified before. Conversely, devices whose machine-learning systems are not locked could harm one or more groups over time if they’re evolving by using mostly data from a different group. What’s more, identifying the point at which the device gets comparatively worse at treating one group can be hard. A Tool Kit for Executives. So how should executives manage the existing and emerging risks of machine learning? Developing appropriate processes, increasing the savviness of management and the board, asking the right questions, and adopting the correct mental frame are important steps. Treat machine learning as if it’s human. Executives need to think of machine learning as a living entity, not an inanimate technology. Just as cognitive testing of employees won’t reveal how they’ll do when added to a preexisting team in a business, laboratory testing cannot predict the performance of machine-learning systems in the real world. Executives should demand a full analysis of how employees, customers, or other users will apply these systems and react to their decisions. Even when not required to do so by regulators, companies may want to subject their new machine-learning-based products to randomized controlled trials to ensure their safety, efficacy, and fairness prior to rollout. But they may also want to analyze products’ decisions in the actual market, where there are various types of users, to see whether the quality of decisions differs across them. In addition, companies should compare the quality of decisions made by the algorithms with those made in the same situations without employing them. Before deploying products at scale, especially but not only those that haven’t undergone randomized controlled trials, companies should consider testing them in limited markets to get a better idea of their accuracy and behavior when various factors are at play—for instance, when users don’t have equal expertise, the data from sources varies, or the environment changes. Failures in real-world settings signal the need to improve or retire algorithms. Think like a regulator and certify first. Businesses should develop plans for certifying machine-learning offerings before they go to market. The practices of regulators offer a good road map. In 2019, for example, the FDA published a discussion paper that proposed a new regulatory framework for modifications to machine-learning-based software as a medical device. It laid out an approach that would allow such software to continuously improve while maintaining the safety of patients, which included a complete assessment of the company—or team—developing the software to ensure it had a culture of organizational excellence and high quality that would lead it to regularly test its machine-learning devices. If companies don’t adopt such certification processes, they may expose themselves to liability—for example, for performing insufficient due diligence. Many start-ups provide services to certify that products and processes don’t suffer from bias, prejudice, stereotypes, unfairness, and other pitfalls. Professional organizations, such as the Institute of Electrical and Electronics Engineers and the International Organization for Standardization, are also developing standards for such certification, while companies like Google offer AI ethics services that examine multiple dimensions, ranging from the data used to train systems, to their behavior, to their impact on well-being. Companies might need to develop similar frameworks of their own. Gregory Reid/Gallery Stock Monitor continuously. As machine-learning-based products and services and the environments they operate in evolve, companies may find that their technologies don’t perform as initially intended. It is therefore important that they set up ways to check that these technologies behave within appropriate limits. Other sectors can serve as models. The FDA’s Sentinel Initiative draws from disparate data sources, such as electronic health records, to monitor the safety of medical products and can force them to be withdrawn if they don’t pass muster. In many ways companies’ monitoring programs may be similar to the preventive maintenance tools and processes currently used by manufacturing or energy companies or in cybersecurity. For example, firms might conduct so-called adversarial attacks on AI like those used to routinely test the strength of IT systems’ defenses. Ask the right questions. Executives and regulators need to delve into the following: Accuracy and competitiveness. How much is the performance of the machine-learning-based system likely to improve with the volume of new data from its use if we don’t lock the algorithm? What will such improvements mean for the business? To what extent will consumers understand the benefits and drawbacks of locked versus unlocked systems? Biases. What data was used to train the algorithm? How representative is it of the population on which the algorithm will ultimately operate? Can we predict whether an unlocked algorithm will produce less-biased results than a locked one if we allow it to learn over time? Do the algorithm’s errors affect minorities or other groups in particular? Can a continuous monitoring approach establish “guardrails” that stop the algorithm from becoming discriminatory? The environment. How will the environment in which the offering is used change over time? Are there conditions under which machine learning should not be allowed to make decisions, and if so, what are they? How can we ensure that the offering’s behavior evolves appropriately given how the environment itself is changing? When should we withdraw our offering because the gap between the environment and our offering’s behavior has become too big? What are the boundaries of the environment within which our offering can adapt and operate? How robust and safe are our machine-learning systems throughout their life cycles? Agency. On which third-party components, including data sources, does the behavior of our machine-learning algorithms depend? How much does it vary when they’re used by different types of people—for example, less-skilled ones? What products or services of other organizations use our data or machine-learning algorithms, possibly exposing us to liability? Should we allow other organizations to use machine-learning algorithms that we develop? Develop principles that address your business risks. Businesses will need to establish their own guidelines, including ethical ones, to manage these new risks—as some companies, like Google and Microsoft, have already done. Such guidelines often need to be quite specific (for example, about what definitions of fairness are adopted) to be useful and must be tailored to the risks in question. If you’re using machine learning to make hiring decisions, it would be good to have a model that is simple, fair, and transparent. If you’re using machine learning to forecast the prices of commodity futures contracts, you may care less about those values and more about the maximum potential financial loss allowed for any decision that machine learning makes. Are there conditions under which machine learning should not be allowed to make decisions, and if so, what are they? Luckily, the journey to develop and implement principles doesn’t need to be a lonely one. Executives have a lot to learn from the multiyear efforts of institutions such as the OECD, which developed the first intergovernmental AI principles (adopted in 2019 by many countries). The OECD principles promote innovative, trustworthy, and responsibly transparent AI that respects human rights, the rule of law, diversity, and democratic values, and that drives inclusive growth, sustainable development, and well-being. They also emphasize the robustness, safety, security, and continuous risk management of AI systems throughout their life cycles. The OECD’s recently launched AI Policy Observatory provides further useful resources, such as a comprehensive compilation of AI policies around the world. . . . Machine learning has tremendous potential. But as this technology, along with other forms of AI, is woven into our economic and social fabric, the risks it poses will increase. For businesses, mitigating them may prove as important as—and possibly more critical than—managing the adoption of machine learning itself. If companies don’t establish appropriate practices to address these new risks, they’re likely to have trouble gaining traction in the marketplace. A version of this article appeared in the January–February 2021 issue of Harvard Business Review. Read more on Technology and analytics or related topics Risk management and Information technology and telecom sector BB Boris Babic is an assistant professor of decision sciences at INSEAD. I. Glenn Cohen is a deputy dean, professor of law, and faculty director of the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School. TE Theodoros Evgeniou is a professor at INSEAD. Sara Gerke is an assistant professor of law at Penn State Dickinson Law. She previously worked as a research fellow in medicine, artificial intelligence, and law at the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School. Tweet Post Share Save Get PDF Buy Copies Print Read more on Technology and analytics or related topics Risk management and Information technology and telecom sector Partner Center. Diversity Latest Magazine Ascend Topics Podcasts Video Store The Big Idea Data & Visuals Case Selections
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                            [body] => Machine LearningFilled StarFilled StarFilled StarFilled StarFilled Star4.9stars166,697 ratingsAndrew Ng   Top Instructor  Financial aid available4,593,980 already enrolledOffered ByMachine Learning. Stanford UniversityAbout this Course. 5,349,438 recent viewsMachine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.UserLearner Career Outcomes. Career direction11%. started a new career after completing these coursesCareer Benefit15%. got a tangible career benefit from this courseFlexible deadlinesFlexible deadlinesReset deadlines in accordance to your schedule.Shareable CertificateShareable CertificateEarn a Certificate upon completion100% online100% onlineStart instantly and learn at your own schedule.Hours to completeApprox. 61 hours to completeAvailable languagesEnglishSubtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, German, Russian, English, Hebrew, Spanish, Hindi, JapaneseSkills you will gain. Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine LearningUserLearner Career Outcomes. Career direction11%. started a new career after completing these coursesCareer Benefit15%. got a tangible career benefit from this courseFlexible deadlinesFlexible deadlinesReset deadlines in accordance to your schedule.Shareable CertificateShareable CertificateEarn a Certificate upon completion100% online100% onlineStart instantly and learn at your own schedule.Hours to completeApprox. 61 hours to completeAvailable languagesEnglishSubtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, German, Russian, English, Hebrew, Spanish, Hindi, JapaneseInstructor. Instructor rating 4.93/5 (29,026 Ratings)InfoAndrew NgTop Instructor. InstructorFounder, DeepLearning.AI & Co-founder, Coursera 5,618,880 Learners 32 CoursesOffered by. Stanford University. The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.go to previous testimonialgo to next testimonial123Syllabus - What you will learn from this course. Content RatingThumbs Up97%(1,435,259 ratings)InfoWeek1Week 1. Hours to complete2 hours to completeIntroduction. Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.Hours to complete2 hours to completeReading5 videos (Total 42 min), 9 readings, 1 quizSee AllVideo5 videosWelcome to Machine Learning!1mWelcome6mWhat is Machine Learning?7mSupervised Learning12mUnsupervised Learning14mReading9 readingsMachine Learning Honor Code8mWhat is Machine Learning?5mHow to Use Discussion Forums4mSupervised Learning4mUnsupervised Learning3mWho are Mentors?3mGet to Know Your Classmates8mFrequently Asked Questions11mLecture Slides20mQuiz1 practice exerciseIntroduction30mHours to complete2 hours to completeLinear Regression with One Variable. Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.Hours to complete2 hours to completeReading7 videos (Total 70 min), 8 readings, 1 quizSee AllVideo7 videosModel Representation8mCost Function8mCost Function - Intuition I11mCost Function - Intuition II8mGradient Descent11mGradient Descent Intuition11mGradient Descent For Linear Regression10mReading8 readingsModel Representation3mCost Function3mCost Function - Intuition I4mCost Function - Intuition II3mGradient Descent3mGradient Descent Intuition3mGradient Descent For Linear Regression6mLecture Slides20mQuiz1 practice exerciseLinear Regression with One Variable30mHours to complete2 hours to completeLinear Algebra Review. This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.Hours to complete2 hours to completeReading6 videos (Total 61 min), 7 readings, 1 quizSee AllVideo6 videosMatrices and Vectors8mAddition and Scalar Multiplication6mMatrix Vector Multiplication13mMatrix Matrix Multiplication11mMatrix Multiplication Properties9mInverse and Transpose11mReading7 readingsMatrices and Vectors2mAddition and Scalar Multiplication3mMatrix Vector Multiplication2mMatrix Matrix Multiplication2mMatrix Multiplication Properties2mInverse and Transpose3mLecture Slides10mQuiz1 practice exerciseLinear Algebra30mWeek2Week 2. Hours to complete3 hours to completeLinear Regression with Multiple Variables. What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.Hours to complete3 hours to completeReading8 videos (Total 65 min), 16 readings, 1 quizSee AllVideo8 videosMultiple Features8mGradient Descent for Multiple Variables5mGradient Descent in Practice I - Feature Scaling8mGradient Descent in Practice II - Learning Rate8mFeatures and Polynomial Regression7mNormal Equation16mNormal Equation Noninvertibility5mWorking on and Submitting Programming Assignments3mReading16 readingsSetting Up Your Programming Assignment Environment8mAccess to MATLAB Online and the Exercise Files for MATLAB Users3mInstalling Octave on Windows3mInstalling Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10mInstalling Octave on Mac OS X (10.8 Mountain Lion and Earlier)3mInstalling Octave on GNU/Linux7mMore Octave/MATLAB resources10mMultiple Features3mGradient Descent For Multiple Variables2mGradient Descent in Practice I - Feature Scaling3mGradient Descent in Practice II - Learning Rate4mFeatures and Polynomial Regression3mNormal Equation3mNormal Equation Noninvertibility2mProgramming tips from Mentors10mLecture Slides20mQuiz1 practice exerciseLinear Regression with Multiple Variables30mHours to complete5 hours to completeOctave/Matlab Tutorial. This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.Hours to complete5 hours to completeReading6 videos (Total 80 min), 2 readings, 2 quizzesSee AllVideo6 videosBasic Operations13mMoving Data Around16mComputing on Data13mPlotting Data9mControl Statements: for, while, if statement12mVectorization13mReading2 readingsLecture Slides10mPlease read if you've switched from the original version10mQuiz1 practice exerciseOctave/Matlab Tutorial30mWeek3Week 3. Hours to complete2 hours to completeLogistic Regression. Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.Hours to complete2 hours to completeReading7 videos (Total 71 min), 8 readings, 1 quizSee AllVideo7 videosClassification8mHypothesis Representation7mDecision Boundary14mCost Function10mSimplified Cost Function and Gradient Descent10mAdvanced Optimization14mMulticlass Classification: One-vs-all6mReading8 readingsClassification2mHypothesis Representation3mDecision Boundary3mCost Function3mSimplified Cost Function and Gradient Descent3mAdvanced Optimization3mMulticlass Classification: One-vs-all3mLecture Slides10mQuiz1 practice exerciseLogistic Regression30mHours to complete5 hours to completeRegularization. Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data. Hours to complete5 hours to completeReading4 videos (Total 39 min), 5 readings, 2 quizzesSee AllVideo4 videosThe Problem of Overfitting9mCost Function10mRegularized Linear Regression10mRegularized Logistic Regression8mReading5 readingsThe Problem of Overfitting3mCost Function3mRegularized Linear Regression3mRegularized Logistic Regression3mLecture Slides10mQuiz1 practice exerciseRegularization30mWeek4Week 4. Hours to complete5 hours to completeNeural Networks: Representation. Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks. Hours to complete5 hours to completeReading7 videos (Total 63 min), 6 readings, 2 quizzesSee AllVideo7 videosNon-linear Hypotheses9mNeurons and the Brain7mModel Representation I12mModel Representation II11mExamples and Intuitions I7mExamples and Intuitions II10mMulticlass Classification3mReading6 readingsModel Representation I6mModel Representation II6mExamples and Intuitions I2mExamples and Intuitions II3mMulticlass Classification3mLecture Slides10mQuiz1 practice exerciseNeural Networks: Representation30mShow MoreWeek5Week 5. Hours to complete5 hours to completeNeural Networks: Learning. In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.Hours to complete5 hours to completeReading8 videos (Total 78 min), 8 readings, 2 quizzesSee AllVideo8 videosCost Function6mBackpropagation Algorithm11mBackpropagation Intuition12mImplementation Note: Unrolling Parameters7mGradient Checking11mRandom Initialization6mPutting It Together13mAutonomous Driving6mReading8 readingsCost Function4mBackpropagation Algorithm10mBackpropagation Intuition4mImplementation Note: Unrolling Parameters3mGradient Checking3mRandom Initialization3mPutting It Together4mLecture Slides10mQuiz1 practice exerciseNeural Networks: Learning30mWeek6Week 6. Hours to complete5 hours to completeAdvice for Applying Machine Learning. Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.Hours to complete5 hours to completeReading7 videos (Total 63 min), 7 readings, 2 quizzesSee AllVideo7 videosDeciding What to Try Next5mEvaluating a Hypothesis7mModel Selection and Train/Validation/Test Sets12mDiagnosing Bias vs. Variance7mRegularization and Bias/Variance11mLearning Curves11mDeciding What to Do Next Revisited6mReading7 readingsEvaluating a Hypothesis4mModel Selection and Train/Validation/Test Sets3mDiagnosing Bias vs. Variance3mRegularization and Bias/Variance3mLearning Curves3mDeciding What to do Next Revisited3mLecture Slides10mQuiz1 practice exerciseAdvice for Applying Machine Learning30mHours to complete2 hours to completeMachine Learning System Design. To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.Hours to complete2 hours to completeReading5 videos (Total 60 min), 3 readings, 1 quizSee AllVideo5 videosPrioritizing What to Work On9mError Analysis13mError Metrics for Skewed Classes11mTrading Off Precision and Recall14mData For Machine Learning11mReading3 readingsPrioritizing What to Work On3mError Analysis3mLecture Slides10mQuiz1 practice exerciseMachine Learning System Design30mWeek7Week 7. Hours to complete5 hours to completeSupport Vector Machines. Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.Hours to complete5 hours to completeReading6 videos (Total 98 min), 1 reading, 2 quizzesSee AllVideo6 videosOptimization Objective14mLarge Margin Intuition10mMathematics Behind Large Margin Classification19mKernels I15mKernels II15mUsing An SVM21mReading1 readingLecture Slides10mQuiz1 practice exerciseSupport Vector Machines30mWeek8Week 8. Hours to complete1 hour to completeUnsupervised Learning. We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.Hours to complete1 hour to completeReading5 videos (Total 39 min), 1 reading, 1 quizSee AllVideo5 videosUnsupervised Learning: Introduction3mK-Means Algorithm12mOptimization Objective7mRandom Initialization7mChoosing the Number of Clusters8mReading1 readingLecture Slides10mQuiz1 practice exerciseUnsupervised Learning30mHours to complete5 hours to completeDimensionality Reduction. In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.Hours to complete5 hours to completeReading7 videos (Total 67 min), 1 reading, 2 quizzesSee AllVideo7 videosMotivation I: Data Compression10mMotivation II: Visualization5mPrincipal Component Analysis Problem Formulation9mPrincipal Component Analysis Algorithm15mReconstruction from Compressed Representation3mChoosing the Number of Principal Components10mAdvice for Applying PCA12mReading1 readingLecture Slides10mQuiz1 practice exercisePrincipal Component Analysis30mWeek9Week 9. Hours to complete2 hours to completeAnomaly Detection. Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.Hours to complete2 hours to completeReading8 videos (Total 91 min), 1 reading, 1 quizSee AllVideo8 videosProblem Motivation7mGaussian Distribution10mAlgorithm12mDeveloping and Evaluating an Anomaly Detection System13mAnomaly Detection vs. Supervised Learning7mChoosing What Features to Use12mMultivariate Gaussian Distribution13mAnomaly Detection using the Multivariate Gaussian Distribution14mReading1 readingLecture Slides10mQuiz1 practice exerciseAnomaly Detection30mHours to complete5 hours to completeRecommender Systems. When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.Hours to complete5 hours to completeReading6 videos (Total 58 min), 1 reading, 2 quizzesSee AllVideo6 videosProblem Formulation7mContent Based Recommendations14mCollaborative Filtering10mCollaborative Filtering Algorithm8mVectorization: Low Rank Matrix Factorization8mImplementational Detail: Mean Normalization8mReading1 readingLecture Slides10mQuiz1 practice exerciseRecommender Systems30mWeek10Week 10. Hours to complete2 hours to completeLarge Scale Machine Learning. Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.Hours to complete2 hours to completeReading6 videos (Total 64 min), 1 reading, 1 quizSee AllVideo6 videosLearning With Large Datasets5mStochastic Gradient Descent13mMini-Batch Gradient Descent6mStochastic Gradient Descent Convergence11mOnline Learning12mMap Reduce and Data Parallelism14mReading1 readingLecture Slides10mQuiz1 practice exerciseLarge Scale Machine Learning30mWeek11Week 11. Hours to complete2 hours to completeApplication Example: Photo OCR. Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.Hours to complete2 hours to completeReading5 videos (Total 57 min), 1 reading, 1 quizSee AllVideo5 videosProblem Description and Pipeline7mSliding Windows14mGetting Lots of Data and Artificial Data16mCeiling Analysis: What Part of the Pipeline to Work on Next13mSummary and Thank You4mReading1 readingLecture Slides10mQuiz1 practice exerciseApplication: Photo OCR30mReviews. 4.9Filled StarFilled StarFilled StarFilled StarFilled Star42681 reviews5 stars92.39%4 stars6.89%3 stars0.47%2 stars0.08%1 star0.14%TOP REVIEWS FROM MACHINE LEARNING. Filled StarFilled StarFilled StarFilled StarFilled Starby MNOct 30, 2017Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.Filled StarFilled StarFilled StarFilled StarFilled Starby EJMar 26, 2018Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.Filled StarFilled StarFilled StarFilled StarStarby AMAug 8, 2018The content is super useful but i think this course need to be recorded again because some materials has more easier ways to illustrate even by Prof Andrew which has illustrate it in deep learning .aiFilled StarFilled StarFilled StarFilled StarFilled Starby ASOct 22, 2020This course is awesome. Learning material is easy to understand. Before this course i tried to read some technic literature about ML and it was scary, but in this course i enjoyed by learning process.View all reviewsFrequently Asked Questions. When will I have access to the lectures and assignments?Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option: The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.What will I get if I purchase the Certificate?When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.Is financial aid available?Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You’ll be prompted to complete an application and will be notified if you are approved. Learn more.More questions? Visit the Learner Help Center. Coursera Footer. Start or advance your career. 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                            [title] => How Do You Become a Machine Learning Engineer?
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                            [body] => How Do You Become a Machine Learning Engineer?Machine learning is a rapidly growing field that is integral to the development of artificial intelligence. Learn more about how to become a machine learning engineer here. Here’s what we’ll cover: What is a machine learning engineer? What does a machine learning engineer do?  Machine learning engineer job description, skills, & more How to become a machine learning engineer in 6 steps How can Springboard help you become an ML engineer? Machine learning engineer FAQs Whenever you’re browsing film and television recommendations on Netflix, encountering ads on social media that are relevant to your interests or search history, or voicing commands to Amazon’s Alexa or Apple’s Siri, you’re directly interacting with the work of a machine learning engineer. While it takes a lot of dedication to gain both the data science and computer science fundamentals needed to become a machine learning engineer, there is a rewarding payoff: machine learning engineers are part of a rapidly evolving field that works at the forefront of deep learning and artificial intelligence and have a growing impact on the efficiency and innovation of industries ranging from entertainment to retail, healthcare, finance, tech, and auto.  Read on to find out more about how to become a machine learning engineer. What is a machine learning engineer? A machine learning engineer is a computer programmer who designs and builds self-running software that learns from data and automates predictive models. Because of the interdisciplinary nature of the job—needing both an understanding of data models and data structures and the ability to deploy those models in usable software—machine learning engineers sit at the intersection of software engineering and data science, possessing skills from both disciplines.  For this reason, machine learning engineers are in high demand across all industries that are heavy on automation, rely on big data, or are searching for ways to make their systems and services more efficient. What does a machine learning engineer do? . Machine learning engineers teach software and systems to learn on their own without human intervention. Think about YouTube and Netflix’s recommendation engines; Amazon’s purchasing suggestions; social media apps and platforms being able to detect spam or inappropriate content on their own. In action, this involves performing dozens of different tasks, such as running machine learning experiments using programming languages and machine learning libraries; deploying machine learning solutions into production; optimizing those solutions for performance and scalability; implementing custom machine learning code; performing foundational data science work such as analyzing data and coming up with use cases; and performing foundational data engineering work by ensuring a good flow between databases and backend systems. A large part of the job also requires collaboration with other stakeholders such as data scientists and researchers, software engineers, and product managers to define project goals, roadmaps, and how each professional’s work can inform the work of others. Machine learning engineer job description, skills, & more. While job descriptions for machine learning engineers will vary depending on industry, organization, and team, a typical machine learning engineering job description will emphasize the need for someone who can design and train computers to learn automatically. Underscoring this skill set is a background in both data science and software engineering. On the data science front, expected skills include proficiency with programming languages such as Python, SQL, and Java; the ability to perform 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.), an understanding of variance, correlations, and dynamic programming, and being able to develop an evaluation strategy for predictive models and machine learning algorithms.  On the software engineering front, expected skills include proficiency with system design; understanding data structures such as stacks, queues, graphs, trees, and multi-dimensional arrays; understanding computability, complexity, and approximate algorithms; and knowledge of computer architecture such as memory, clusters, bandwidth, deadlocks, and cache. Learn more about a machine learning engineer job description, skills, and more here. How to become a machine learning engineer in 6 steps. Machine learning engineering is a relatively new and constantly evolving field. Because of this, there is no 'right' way to become a machine learning engineer. There are multiple ways to get into the field depending on your educational background, technical skills, and areas of interest. The steps below outline how you can get hired as a machine learning engineer. Understand your end goal. Before you decide to pursue a bachelor’s or master’s degree or enroll in an online bootcamp, it’s important to be clear about what you want out of a career in machine learning engineering so that you can determine the best path to take. Some careers in machine learning will require a  bachelor’s degree in computer science, mathematics, statistics, or a related field, while others will require you to go further and obtain a master’s degree or Ph.D. Others yet will determine eligibility based on work experience and the transferability of your skills. Either way, preparing for a career in machine learning engineering takes hard work and commitment, so it’s important to understand your end goal. Learn software engineering fundamentals. Machine learning engineers write the code that powers systems and programs, so they need to be deeply familiar with both an array of programming languages (Python, Java, and C++ are the most popular) and foundational computer science so that they can build and deploy software. Learn data science fundamentals. One of the key things that set machine learning engineers apart from traditional software engineers is their overlap with data scientists. In addition to developing a solid software engineering skillset, anyone interested in machine learning engineering should know how to find, clean, optimize, and query data sets; understand data models; and bridge the findings from data science with the building blocks of software engineering. Familiarize yourself with the tools and concepts. In addition to learning programming languages, it helps to familiarize yourself with commonly used machine learning infrastructure and concepts. For example, machine learning engineers working with AI and deep learning will likely use tools such as TensorFlow, Spark and Hadoop, R Programming, Apache Kafka, Weka, and MATLAB. ML engineers tasked with training virtual assistants or chatbots will likely need to understand natural language processing, neural networks, regression models, and informational retrieval. Work on real-life projects. The most important part of becoming a machine learning engineer is understanding how to apply your theoretical knowledge to actual tasks and assignments. Completing a machine learning engineering project end-to-end and documenting it in a portfolio will show future employers your ability to understand and deliver at every step of a project. Do an online course or bootcamp. While some machine learning engineers find success in completing these steps on their own, many benefit from additional support. For this reason, candidates often turn to an online bootcamp for a comprehensive and supported approach to learning ML engineering. How can Springboard help you become an ML engineer? Want to know how to get into machine learning engineering or a related field? Springboard’s Machine Learning Engineering Career track is comprehensive, production-focused, and designed for people with strong software engineering or computer programming skills who want to become machine learning engineers. The online, six-month curriculum will help you master key aspects of machine learning engineering such as machine learning models, deep learning, computer vision image processing, the machine learning engineering stack, and working with data. Most importantly, 50% of the course is focused on production engineering skills, which ensures that you not only graduate with a solid understanding of machine learning and deep learning concepts, but you also get the hands-on experience that hiring managers value. In addition, you will learn to: Design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or a web service Understand and work with common deep neural network configurations, linear algebra and calculus, and engineering frameworks such as Keras, TensorFlow, PyTorch, Fast.ai, and CuPy Work with object detection and image segmentation techniques Create reliable and reproducible data pipelines to ensure your model is well fueled Use data science, machine learning, and software engineering tools Perform classification modeling, regression modeling, optimization, and build recommendation systems As with all of Springboard’s courses, you will also receive unlimited one-on-one support from an industry mentor, get experience working on real-world projects, and have access to career support and a job guarantee in a machine learning engineer role after completing the course.  Machine learning engineer FAQs. Want to know more about how to get into machine learning engineering? Read on to find the answers to some frequently asked questions. What should a machine learning engineer know? The first requirement is to have a strong grasp of computer science and data science skills, which means learning programming languages such as C++, Python, R, SQL, and Java, and tools such as MapReduce, TensorFlow, and Spark. You should also be familiar with both the concepts and application of statistics, mathematics, neural network architecture, signal processing techniques, data structures, memory management, and AI training. Are machine learning engineers in demand? Machine learning engineering offers strong career stability and varied opportunities because it is in such high demand across multiple industries—the profession saw a 344% increase in job listings from 2015-2018, and this number is expected to rise in the coming years as more organizations realize the potential of marrying big data with software. Can you learn machine learning without coding? If you want to get into machine learning engineering, you’ll have to learn how to code. There’s no way around it—many machine learning tasks require at least a familiarity with programming languages such as Python, Javascript, R, or C++. While it is possible to learn and understand some machine learning concepts without dabbling in code, a machine learning engineer who wants to implement machine learning models that tackle real-world problems will have to have a strong coding background. In fact, even having basic programming knowledge will open doors in machine learning because it will make accessible graphical and scripting ML environments such as Weka, Orange, and BigML, as well as machine learning libraries, which will allow you to perform complex tasks without having to write too much code. Is machine learning engineering a good career? Machine learning engineering is a high-paying, in-demand profession that is seeing rapid job growth and generous salaries. Indeed ranked the profession number one in 2019 based on the number of open roles and the average compensation—344% growth in job postings from 2015-2018; an average base salary of around $146,085—describing it as an “extremely promising position.” In addition to job security, machine learning engineering offers enormous variety and industry flexibility because machine learning engineers are needed across many different sectors, from government and healthcare to entertainment and tech, to finance and retail. Can you become a machine learning engineer without a degree? Most machine learning engineering positions require at least a bachelor’s degree or master’s degree in computer science, mathematics, statistics, or a related field. But the key determining factor in whether a person lands a machine learning engineering role is whether they have the knowledge, experience, and portfolio of projects to prove that they can get the job done. For this reason, it is not unheard of for a candidate without a degree to get hired if they can show that they have relevant experience. It’s also not uncommon for candidates who hold degrees in unrelated fields to retrain—either through short courses, online bootcamps, or self-study—to pick up the relevant skills and experience and begin their careers in machine learning engineering. How long does it take to become a machine learning engineer? It takes approximately six months to complete a machine learning engineering curriculum. If an individual is starting without any prior knowledge of computer programming, data science, or statistics, it can take longer. Springboard’s Machine Learning Engineering Career Track takes 6 months to complete. Springboard’s Data Science Prep Course, which teaches foundational programming and statistics, takes 4-6 weeks to complete. Is it hard to become a machine learning engineer? Becoming a machine learning engineer requires commitment. The role is multidisciplinary, requiring the technical development skills of a software engineer and the analytical skills of a data scientist. Those who have a background in computer science, artificial intelligence, software development, statistics, data science, or data engineering will have a head start, but it is not uncommon for individuals to begin from scratch and be working in machine learning within a few years. After all, machine learning and artificial intelligence are relatively new and frequently evolving fields, which means there’s always more to learn—and plenty of room for newcomers. 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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                            [body] => Machine learning, explained By Sara Brown Apr 21, 2021 Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images.  When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed. “In just the last five or 10 years, machine learning has become a critical way, arguably the most important way, most parts of AI are done,” said MIT Sloan professorThomas W. Malone, the founding director of the MIT Center for Collective Intelligence. “So that's why some people use the terms AI and machine learning almost as synonymous … most of the current advances in AI have involved machine learning.” With the growing ubiquity of machine learning, everyone in business is likely to encounter it and will need some working knowledge about this field. A 2020 Deloitte survey found that 67% of companies are using machine learning, and 97% are using or planning to use it in the next year. From manufacturing to retail and banking to bakeries, even legacy companies are using machine learning to unlock new value or boost efficiency. “Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations,” said MIT computer science professor Aleksander Madry, director of the MIT Center for Deployable Machine Learning. While not everyone needs to know the technical details, they should understand what the technology does and what it can and cannot do, Madry added. “I don’t think anyone can afford not to be aware of what’s happening.” That includes being aware of the social, societal, and ethical implications of machine learning. “It's important to engage and begin to understand these tools, and then think about how you're going to use them well. We have to use these [tools] for the good of everybody,” said Dr. Joan LaRovere, MBA ’16, a pediatric cardiac intensive care physician and co-founder of the nonprofit The Virtue Foundation. “AI has so much potential to do good, and we need to really keep that in our lenses as we're thinking about this. How do we use this to do good and better the world?” What is machine learning? . Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems. The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world. Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.” The definition holds true, according toMikey Shulman, a lecturer at MIT Sloan and head of machine learning at Kensho, which specializes in artificial intelligence for the finance and U.S. intelligence communities. He compared the traditional way of programming computers, or “software 1.0,” to baking, where a recipe calls for precise amounts of ingredients and tells the baker to mix for an exact amount of time. Traditional programming similarly requires creating detailed instructions for the computer to follow. But in some cases, writing a program for the machine to follow is time-consuming or impossible, such as training a computer to recognize pictures of different people. While humans can do this task easily, it’s difficult to tell a computer how to do it. Machine learning takes the approach of letting computers learn to program themselves through experience.  Machine learning starts with data — numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program. From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find patterns or make predictions. Over time the human programmer can also tweak the model, including changing its parameters, to help push it toward more accurate results. (Research scientist Janelle Shane’s website AI Weirdness is an entertaining look at how machine learning algorithms learn and how they can get things wrong — as happened when an algorithm tried to generate recipes and created Chocolate Chicken Chicken Cake.) Some data is held out from the training data to be used as evaluation data, which tests how accurate the machine learning model is when it is shown new data. The result is a model that can be used in the future with different sets of data. Successful machine learning algorithms can do different things, Malone wrote in a recent research brief about AI and the future of work that was co-authored by MIT professor and CSAIL director Daniela Rus and Robert Laubacher, the associate director of the MIT Center for Collective Intelligence. “The function of a machine learning system can be descriptive, meaning that the system uses the data to explain what happened; predictive, meaning the system uses the data to predict what will happen; or prescriptive, meaning the system will use the data to make suggestions about what action to take,” the researchers wrote.   There are three subcategories of machine learning: Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today. In unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren’t explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases. Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take. x x Source: Thomas Malone | MIT Sloan. See: https://bit.ly/3gvRho2, Figure 2.   In the Work of the Future brief, Malone noted that machine learning is best suited for situations with lots of data — thousands or millions of examples, like recordings from previous conversations with customers, sensor logs from machines, or ATM transactions. For example, Google Translate was possible because it “trained” on the vast amount of information on the web, in different languages. In some cases, machine learning can gain insight or automate decision-making in cases where humans would not be able to, Madry said. “It may not only be more efficient and less costly to have an algorithm do this, but sometimes humans just literally are not able to do it,” he said. Google search is an example of something that humans can do, but never at the scale and speed at which the Google models are able to show potential answers every time a person types in a query, Malone said. “That’s not an example of computers putting people out of work. It's an example of computers doing things that would not have been remotely economically feasible if they had to be done by humans.” Machine learning is also associated with several other artificial intelligence subfields: Natural language processing Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa. Neural networks Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers. In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat. Deep learning Deep learning networks are neural networks with many layers. The layered network can process extensive amounts of data and determine the “weight” of each link in the network — for example, in an image recognition system, some layers of the neural network might detect individual features of a face, like eyes, nose, or mouth, while ano