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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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                            [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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                                    [0] => Reply Sandis December 22, 2017 at 9:36 pm # Could you kindly provide any example from your own experience?
                                    [1] => Not only the resulting design but also your thought process and how you came about the resulting design?
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                            [body] => Why Applied Machine Learning Is Hard By Jason Brownlee on December 22, 2017 in Machine Learning Process Tweet Tweet Share Share How to Handle the Intractability of Applied Machine Learning. Applied machine learning is challenging. You must make many decisions where there is no known “right answer” for your specific problem, such as: What framing of the problem to use? What input and output data to use? What learning algorithm to use? What algorithm configuration to use? This is challenging for beginners that expect that you can calculate or be told what data to use or how to best configure an algorithm. In this post, you will discover the intractable nature of designing learning systems and how to deal with it. After reading this post, you will know: How to develop a clear definition of your learning problem for yourself and others. The 4 decision points you must consider when designing a learning system for your problem. The 3 strategies that you can use to specifically address the intractable problem of designing learning systems in practice. Let’s get started. Overview. This post is divided into 6 sections inspired by chapter 1 of Tom Mitchell’s excellent 1997 book Machine Learning; they are: Well-Posed Learning Problems Choose the Training Data Choose the Target Function Choose a Representation of the Target Function Choose a Learning Algorithm How to Design Learning Systems Well-Posed Learning Problems. We can define a general learning task in the field of applied machine learning as a program that learns from experience on some task against a specific performance measure. Tom Mitchell in his 1997 book Machine Learning states this clearly as: 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. — Page 2, Machine Learning, 1997. We take this as a general definition for the types of learning tasks that we may be interested in for applied machine learning such as predictive modeling. Tom lists a few examples to make this clear, such as: Learning to recognize spoken words. Learning to drive an autonomous vehicle. Learning to classify new astronomical structures. Learning to play world-class backgammon. We can use the above definition to define our own predictive modeling problem. Once defined, the task becomes that of designing a learning system to address it. Designing a learning system, i.e. an application of machine learning, involves four design choices: Choosing the training data. Choosing the target function. Choosing the representation. Choosing the learning algorithm. There might be a best set of choices that you can make for a given problem given infinite resources, but we don’t have infinite time, compute resources, and knowledge about the domain or learning systems. Therefore, although we can prepare a well-posed description of a learning problem, designing the best possible learning system is intractable. The best we can do is use knowledge, skill, and available resources to work through the design choices. Let’s look at each of these design choices in more detail. Choose the Training Data. You must choose the data your learning system will use as experience from which to learn. This is the data from past observations. The type of training experience available can have a significant impact on success or failure of the learner. — Page 5, Machine Learning, 1997. It is rarely well formatted and ready to use; often you must collect the data you need (or think you might need) for the learning problem. This may mean: Scraping documents. Querying databases. Processing files. Collating disparate sources Consolidating entities. You need to get all of the data together and into a normalized form such that one observation represents one entity for which an outcome is available. Choose the Target Function. Next, you must choose the framing of the learning problem. Machine learning is really a problem of learning a mapping function (f) from inputs (X) to outputs (y). y = f(X) 1 y = f(X) This function can then be used on new data in the future in order to predict the most likely output. The goal of the learning system is to prepare a function that best maps inputs to outputs given the resources available. The underlying function that actually exists is unknown. If we knew the form of this function, we could use it directly and we would not need machine learning to learn it. More generally, this is a problem called function approximation. The result will be an approximation, meaning that it will have error. We will do our best to minimize this error, but some error will always exist given noise in the data. … we have reduced the learning task in this case to the problem of discovering an operational description of the ideal target function V. It may be very difficult in general to learn such an operational form of V perfectly. In fact, we often expect learning algorithms to acquire only some approximation to the target function, and for this reason the process of learning the target function is often called function approximation. — Page 8, Machine Learning, 1997. This step is about selecting exactly what data to use as input to the function, e.g. the input features or input variables and what exactly will be predicted, e.g. the output variable. Often, I refer to this as the framing of your learning problem. Choosing the inputs and outputs essentially chooses the nature of the target function we will seek to approximate. Choose a Representation of the Target Function. Next, you must choose the representation you wish to use for the mapping function. Think of this as the type of final model you wish to have that you can then use to make predictions. You must choose the form of this model, the data structure if you’d like. Now that we have specified the ideal target function V, we must choose a representation that the learning program will use to describe the function Vprime that it will learn. — Page 8, Machine Learning, 1997. For example: Perhaps your project requires a decision tree that is easy to understand and explain to stakeholders. Perhaps your stakeholders prefer a linear model that the stats guys can easily interpret. Perhaps your stakeholders don’t care about anything other than model performance so all model representations are up for grabs. The choice of representation will impose constraints on the types of learning algorithms that you can use to learn the mapping function. Choose a Learning Algorithm. Finally, you must choose the learning algorithm that will take the input and output data and learn a model of your preferred representation. If there were few constraints on the choice of representation, as is often the case, then you may be able to evaluate a suite of different algorithms and representations. If there were strong constraints on the choice of function representation, e.g. a weighted sum linear model or a decision tree, then the choice of algorithms will be limited to those that that can operate on the specific representations. The choice of algorithm may impose its own constraints, such as specific data preparation transforms like data normalization. How to Design Learning Systems. Developing a learning system is challenging. No one can tell you the best answer to each decision along the way; the best answer is unknown for your specific learning problem. Mitchell helps to clarify this with a depiction of the choices made in designing a learning system for playing checkers. The depiction of Choices in Designing a Checker-Playing Learning System.Taken from “Machine Learning”, 1997. The choices act as points of constraint on the design process. Mitchell goes on to say: These design choices have constrained the learning task in a number of ways. We have restricted the type of knowledge that can be acquired to a single linear evaluation function. Furthermore, we have constrained this evaluation function to depend on only the six specific board features provided. If the true target function V can indeed be represented by a linear combination of these particular features, then our program has a good chance to learn it. If not, then the best we can hope for is that it will learn a good approximation, since a program can certainly never learn anything that it cannot at least represent. — Pages 13-14, Machine Learning, 1997. I like this passage as it really drives home both the importance of these constraints to simplify the problem, and the risk of making choices that limit or prevent the system from learning the problem sufficiently. Generally, you cannot analytically calculate the answer to these choices, e.g. what data to use, what algorithm to use, or what algorithm configuration to use. Nevertheless, all is not lost; here are 3 tactics that you can use in practice: Copy. Look to the literature or experts for learning systems on problems the same or similar to your problem and copy the design of the learning system. It is very likely you are not the first to work on a problem of a given type. At the very worst, the copied design provides a starting point for your own design. Search. List available options at each decision point and empirically evaluate each to see what works best on your specific data. This may be the most robust and most practiced approach in applied machine learning. Design. After completing many projects via the Copy and Search methods above, you will develop an intuition for how to design machine learning systems. Developing learning systems is not a science; it is engineering. Developing new machine learning algorithms and describing how and why they work is a science, and this is often not required when developing a learning system. Developing a learning system is a lot like developing software. You must combine (1) copies of past designed that work, (2) prototypes that show promising results, and (3) design experience when developing a new system in order to get the best results. Further Reading. This section provides more resources on the topic if you are looking to go deeper. Chapter 1, Machine Learning, 1997. Tom Mitchell’s homepage Well Posed Problem on Wikipedia Intractability on Wikipedia How to Define Your Machine Learning Problem Applied Machine Learning Process Summary. In this post, you discovered the intractable nature of designing learning systems in applied machine learning and how to deal with it. Specifically, you learned: How to develop a clear definition of your learning problem for yourself and others. The 4 decision points you must consider when designing a learning system for your problem. The 3 strategies that you can use to specifically address the intractable problem of designing learning systems in practice. 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. 14 Different Types of Learning in Machine LearningWhy Do I Get Different Results Each Time in Machine…Machine Learning BooksMachine Learning for DevelopersThe Machine Learning Mastery MethodHow to Develop Voting Ensembles With Python A Gentle Introduction to Transfer Learning for Deep Learning A Gentle Introduction to Applied Machine Learning as a Search Problem Leave a Reply Click here to cancel reply.
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                            [title] => The Limitations of Machine Learning | by Matthew Stewart, PhD Researcher | Towards Data Science
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                                    [6] => ML Intro 6: Reinforcement Learning for non-Differentiable Functions
                                    [7] => Machine Learning Foundations: Learning the Fundamentals
                                    [8] => Seasonal Contrast: Transferable Visual Representations for Remote Sensing using PyTorch Lightning
                                    [9] => My first neural network
                                    [10] => The Optimal Classifier
                                    [11] => Writing Travel Blogs with Deep Learning
                                    [12] => Speak to the Dead with Deep Learning
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                            [body] => The Limitations of Machine LearningMachine learning is now seen as a silver bullet for solving all problems, but sometimes it is not the answer.Matthew Stewart, PhD ResearcherJul 29, 2019·12 min read“If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”— Andrew NgMost people reading this are likely familiar with machine learning and the relevant algorithms used to classify or predict outcomes based on data. However, it is important to understand that machine learning is not the answer to all problems. Given the usefulness of machine learning, it can be hard to accept that sometimes it is not the best solution to a problem.In this article, I aim to convince the reader that there are times when machine learning is the right solution, and times when it is the wrong solution.Machine learning, a subset of artificial intelligence, has revolutionalized the world as we know it in the past decade. The information explosion has resulted in the collection of massive amounts of data, especially by large companies such as Facebook and Google. This amount of data, coupled with the rapid development of processor power and computer parallelization, has now made it possible to obtain and study huge amounts of data with relative ease.Nowadays, hyperbole about machine learning and artificial intelligence is ubiquitous. This is perhaps rightly so, given the potential for this field is massive. The number of AI consulting agencies has soared in the past few years, and, according to a report from Indeed, the number of jobs related to AI ballooned by 100% between 2015 and 2018.As of December 2018, Forbes found that 47% of business had at least one AI capability in their business process, and a report by Deloitte projects that a penetration rate of enterprise software with AI built-in, and cloud-based AI development services, will reach an estimated 87 and 83 percent respectively. These numbers are impressive — if you are planning to change careers anytime soon, AI seems like a pretty good bet.So it all seems great right? Companies are happy and, presumably, consumers are also happy — otherwise, the companies would not be using AI.It is great, and I am a huge fan of machine learning and AI. However, there are times when using machine learning is just unnecessary, does not make sense, and other times when its implementation can get you into difficulties.Limitation 1 — EthicsMachine learning, a subset of artificial intelligence, has revolutionalized the world as we know it in the past decade. The information explosion has resulted in the collection of massive amounts of data, especially by large companies such as Facebook and Google. This amount of data, coupled with the rapid development of processor power and computer parallelization, has now made it possible to obtain and study huge amounts of data with relative ease.It is easy to understand why machine learning has had such a profound impact on the world, what is less clear is exactly what its capabilities are, and perhaps more importantly, what its limitations are. Yuval Noah Harari famously coined the term ‘dataism’, which refers to a putative new stage of civilization we are entering in which we trust algorithms and data more than our own judgment and logic.Whilst you may find this idea laughable, remember the last time you went on vacation and followed the instructions of a GPS rather than your own judgment on a map — do you question the judgment of the GPS? People have literally driven into lakes because they blindly followed the instructions from their GPS.The idea of trusting data and algorithms more than our own judgment has its pros and cons. Obviously, we benefit from these algorithms, otherwise, we wouldn’t be using them in the first place. These algorithms allow us to automate processes by making informed judgments using available data. Sometimes, however, this means replacing someone’s job with an algorithm, which comes with ethical ramifications. Additionally, who do we blame if something goes wrong?The most commonly discussed case currently is self-driving cars — how do we choose how the vehicle should react in the event of a fatal collision? In the future will we have to select which ethical framework we want our self-driving car to follow when we are purchasing the vehicle?If my self-driving car kills someone on the road, whose fault is it?Whilst these are all fascinating questions, they are not the main purpose of this article. Clearly, however, machine learning cannot tell us anything about what normative values we should accept, i.e. how we should act in the world in a given situation. As David Hume famously said, one cannot ‘derive an ought from an is’.Limitation 2 — Deterministic ProblemsThis is a limitation I personally have had to deal with. My field of expertise is environmental science, which relies heavily on computational modeling and using sensors/IoT devices.Machine learning is incredibly powerful for sensors and can be used to help calibrate and correct sensors when connected to other sensors measuring environmental variables such as temperature, pressure, and humidity. The correlations between the signals from these sensors can be used to develop self-calibration procedures and this is a hot research topic in my research field of atmospheric chemistry.However, things get a bit more interesting when it comes to computational modeling.Running computer models that simulate global weather, emissions from the planet, and transport of these emissions is very computationally expensive. In fact, it is so computationally expensive, that a research-level simulation can take weeks even when running on a supercomputer.Good examples of this are MM5 and WRF, which are numerical weather prediction models that are used for climate research and for giving you weather forecasts on the morning news. Wonder what weather forecasters do all day? Run and study these models.Running weather models is fine, but now that we have machine learning, can we just use this instead to obtain our weather forecasts? Can we leverage data from satellites, weather stations, and use an elementary predictive algorithm to discern whether it is going to rain tomorrow?The answer is, surprisingly, yes. If we have knowledge of the air pressures around a certain region, the levels of moisture in the air, wind speeds, and information about neighboring points and their own variables, it becomes possible to train, for example, a neural network. But at what cost?Using a neural network with a thousand inputs to determine whether it will rain tomorrow in Boston is possible. However, utilizing a neural network misses the entire physics of the weather system.Machine learning is stochastic, not deterministic.A neural network does not understand Newton’s second law, or that density cannot be negative — there are no physical constraints.However, this may not be a limitation for long. There are multiple researchers looking at adding physical constraints to neural networks and other algorithms so that they can be used for purposes such as this.Limitation 3 — DataThis is the most obvious limitation. If you feed a model poorly, then it will only give you poor results. This can manifest itself in two ways: lack of data, and lack of good data.Lack of DataMany machine learning algorithms require large amounts of data before they begin to give useful results. A good example of this is a neural network. Neural networks are data-eating machines that require copious amounts of training data. The larger the architecture, the more data is needed to produce viable results. Reusing data is a bad idea, and data augmentation is useful to some extent, but having more data is always the preferred solution.If you can get the data, then use it.Lack of Good DataDespite the appearance, this is not the same as the above comment. Let’s imagine you think you can cheat by generating ten thousand fake data points to put in your neural network. What happens when you put it in?It will train itself, and then when you come to test it on an unseen data set, it will not perform well. You had the data but the quality of the data was not up to scratch.In the same way that having a lack of good features can cause your algorithm to perform poorly, having a lack of good ground truth data can also limit the capabilities of your model. No company is going to implement a machine learning model that performs worse than human-level error.Similarly, applying a model that was trained on a set of data in one situation may not necessarily apply as well to a second situation. The best example of this I have found so far is in breast cancer prediction.Mammography databases have a lot of images in them, but they suffer from one problem that has caused significant issues in recent years — almost all of the x-rays are from white women. This may not sound like a big deal, but actually, black women have been shown to be 42 percent more likely to die from breast cancer due to a wide range of factors that may include differences in detection and access to health care. Thus, training an algorithm primarily on white women adversely impacts black women in this case.What is needed in this specific case is a larger number of x-rays of black patients in the training database, more features relevant to the cause of this 42 percent increased likelihood, and for the algorithm to be more equitable by stratifying the dataset along the relevant axes.If you are skeptical of this or would like to know more, I recommend you look at this article.Limitation 4 — MisapplicationRelated to the second limitation discussed previously, there is purported to be a “crisis of machine learning in academic research” whereby people blindly use machine learning to try and analyze systems that are either deterministic or stochastic in nature.For reasons discussed in limitation two, applying machine learning on deterministic systems will succeed, but the algorithm which not be learning the relationship between the two variables, and will not know when it is violating physical laws. We simply gave some inputs and outputs to the system and told it to learn the relationship — like someone translating word for word out of a dictionary, the algorithm will only appear to have a facile grasp of the underlying physics.For stochastic (random) systems, things are a little less obvious. The crisis of machine learning for random systems manifests itself in two ways:P-hackingScope of the analysisP-hackingWhen one has access to large data, which may have hundreds, thousands, or even millions of variables, it is not too difficult to find a statistically significant result (given that the level of statistical significance needed for most scientific research is p < 0.05). This often leads to spurious correlations being found that are usually obtained by p-hacking (looking through mountains of data until a correlation showing statistically significant results is found). These are not true correlations and are just responding to the noise in the measurements.This has resulted in individuals ‘fishing’ for statistically significant correlations through large data sets, and masquerading these as true correlations. Sometimes, this is an innocent mistake (in which case the scientist should be better trained), but other times, it is done to increase the number of papers a researcher has published — even in the world of academia, competition is strong and people will do anything to improve their metrics.Scope of the AnalysisThere are inherent differences in the scope of the analysis for machine learning as compared with statistical modeling — statistical modeling is inherently confirmatory, and machine learning is inherently exploratory.We can consider confirmatory analysis and models to be the kind of thing that someone does in a Ph.D. program or in a research field. Imagine you are working with an advisor and trying to develop a theoretical framework to study some real-world system. This system has a set of pre-defined features that it is influenced by, and, after carefully designing experiments and developing hypotheses you are able to run tests to determine the validity of your hypotheses.Exploratory, on the other hand, lacks a number of qualities associated with the confirmatory analysis. In fact, in the case of truly massive amounts of data and information, the confirmatory approaches completely break down due to the sheer volume of data. In other words, it simply is not possible to carefully lay out a finite set of testable hypotheses in the presence of hundreds, much less thousands, much less millions of features.Therefore and, again, broadly speaking, machine learning algorithms and approaches are best suited for exploratory predictive modeling and classification with massive amounts of data and computationally complex features. Some will contend that they can be used on “small” data but why would one do so when classic, multivariate statistical methods are so much more informative?ML is a field which, in large part, addresses issues derived from information technology, computer science, and so on, these can be both theoretical and applied problems. As such, it is related to fields such as physics, mathematics, probability, and statistics but ML is really a field unto itself, a field which is unencumbered by the concerns raised in the other disciplines. Many of the solutions ML experts and practitioners come up with are painfully mistaken…but they get the job done.Limitation 5 — InterpretabilityInterpretability is one of the primary problems with machine learning. An AI consultancy firm trying to pitch to a firm that only uses traditional statistical methods can be stopped dead if they do not see the model as interpretable. If you cannot convince your client that you understand how the algorithm came to the decision it did, how likely are they to trust you and your expertise?As bluntly stated in “Business Data Mining — a machine learning perspective”:“A business manager is more likely to accept the [machine learning method] recommendations if the results are explained in business terms”These models as such can be rendered powerless unless they can be interpreted, and the process of human interpretation follows rules that go well beyond technical prowess. For this reason, interpretability is a paramount quality that machine learning methods should aim to achieve if they are to be applied in practice.The blossoming -omics sciences (genomics, proteomics, metabolomics and the like), in particular, have become the main target for machine learning researchers precisely because of their dependence on large and non-trivial databases. However, they suffer from the lack of interpretability of their methods, despite their apparent success.Summary and Peter Voss’ ListWhile it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as “AI solutionism”. This is the philosophy that, given enough data, machine learning algorithms can solve all of humanity’s problems.As I hope I have made clear in this article, there are limitations that, at least for the time being, prevent that from being the case. A neural network can never tell us how to be a good person, and, at least for now, do not understand Newton’s laws of motion or Einstein’s theory of relativity. There are also fundamental limitations grounded in the underlying theory of machine learning, called computational learning theory, which are primarily statistical limitations. We have also discussed issues associated with the scope of the analysis and the dangers of p-hacking, which can lead to spurious conclusions. There are also issues with the interpretability of results, which can negatively impact businesses that are unable to convince clients and investors that their methods are accurate and reliable.Whilst in this article I have covered very broadly some of the most important limitations of AI, to finish, I will outline a list published in an article by Peter Voss in October 2016, outlining a more comprehensive list on the limitations of AI. Whilst current mainstream techniques can be very powerful in narrow domains, they will typically have some or all of a list of constraints that he sets out and which I’ll quote in full here:Each narrow application needs to be specially trainedRequire large amounts of hand-crafted, structured training dataLearning must generally be supervised: Training data must be taggedRequire lengthy offline/ batch trainingDo not learn incrementally or interactively, in real-timePoor transfer learning ability, reusability of modules, and integrationSystems are opaque, making them very hard to debugPerformance cannot be audited or guaranteed at the ‘long tail’They encode correlation, not causation or ontological relationshipsDo not encode entities or spatial relationships between entitiesOnly handle very narrow aspects of natural languageNot well suited for high-level, symbolic reasoning or planningAll that being said, machine learning and artificial intelligence will continue to revolutionize industry and will only become more prevalent in the coming years. Whilst I recommend you utilize machine learning and AI to their fullest extent, I also recommend that you remember the limitations of the tools you use — after all, nothing is perfect.Newsletter. For updates on new blog posts and extra content, sign up for my newsletter.Newsletter Subscription. Enrich your academic journey by joining a community of scientists, researchers, and industry professionals to obtain…. mailchi.mpMatthew Stewart, PhD Researcher. Environmental/Data Science Ph.D. Candidate at Harvard University | Machine learning consultant at Critical Future | Blogger at TDS. https://mpstewart.netFollow1K 101K 1K 10Machine LearningData ScienceArtificial IntelligenceTowards Data ScienceDeep LearningMore 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. ML Intro 6: Reinforcement Learning for non-Differentiable Functions. Lee Tanenbaum in Towards Data ScienceMachine Learning Foundations: Learning the Fundamentals. Carlos Poles in Analytics VidhyaSeasonal Contrast: Transferable Visual Representations for Remote Sensing using PyTorch Lightning. Oscar Mañas in PyTorch Lightning Developer BlogMy first neural network. Ramin Mohammadi, P.h.DThe Optimal Classifier. Andreas Maier in CodeXWriting Travel Blogs with Deep Learning. Simone Romano in Intuition MachineSpeak to the Dead with Deep Learning. Nicolas Bertagnolli in Towards Data ScienceVIME — Variational Information Maximizing Exploration. Bansi Gajera in Clique Community
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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] => 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 another layer would be able to tell whether those features appear in a way that indicates a face.   Like neural networks, deep learning is modeled on the way the human brain works and powers many machine learning uses, like autonomous vehicles, chatbots, and medical diagnostics. “The more layers you have, the more potential you have for doing complex things well,” Malone said. Deep learning requires a great deal of computing power, which raises concerns about its economic and environmental sustainability. How businesses are using machine learning. Machine learning is the core of some companies’ business models, like in the case of Netflix’s suggestions algorithm or Google’s search engine. Other companies are engaging deeply with machine learning, though it’s not their main business proposition. 6 7 % Share 67% of companies are using machine learning, according to a recent survey. Others are still trying to determine how to use machine learning in a beneficial way. “In my opinion, one of the hardest problems in machine learning is figuring out what problems I can solve with machine learning,” Shulman said. “There’s still a gap in the understanding.”  In a 2018 paper, researchers from the MIT Initiative on the Digital Economy outlined a 21-question rubric to determine whether a task is suitable for machine learning. The researchers found that no occupation will be untouched by machine learning, but no occupation is likely to be completely taken over by it. The way to unleash machine learning success, the researchers found, was to reorganize jobs into discrete tasks, some which can be done by machine learning, and others that require a human. Companies are already using machine learning in several ways, including: Recommendation algorithms. The recommendation engines behind Netflix and YouTube suggestions, what information appears on your Facebook feed, and product recommendations are fueled by machine learning. “[The algorithms] are trying to learn our preferences,” Madry said. “They want to learn, like on Twitter, what tweets we want them to show us, on Facebook, what ads to display, what posts or liked content to share with us.” Image analysis and object detection. Machine learning can analyze images for different information, like learning to identify people and tell them apart — though facial recognition algorithms are controversial. Business uses for this vary. Shulman noted that hedge funds famously use machine learning to analyze the number of cars in parking lots, which helps them learn how companies are performing and make good bets. Fraud detection. Machines can analyze patterns, like how someone normally spends or where they normally shop, to identify potentially fraudulent credit card transactions, log-in attempts, or spam emails. Automatic helplines or chatbots. Many companies are deploying online chatbots, in which customers or clients don’t speak to humans, but instead interact with a machine. These algorithms use machine learning and natural language processing, with the bots learning from records of past conversations to come up with appropriate responses. Self-driving cars. Much of the technology behind self-driving cars is based on machine learning, deep learning in particular. Medical imaging and diagnostics. Machine learning programs can be trained to examine medical images or other information and look for certain markers of illness, like a tool that can predict cancer risk based on a mammogram. Read report: Artificial Intelligence and the Future of Work How machine learning works: promises and challenges . While machine learning is fueling technology that can help workers or open new possibilities for businesses, there are several things business leaders should know about machine learning and its limits. Explainability One area of concern is what some experts call explainability, or the ability to be clear about what the machine learning models are doing and how they make decisions. “Understanding why a model does what it does is actually a very difficult question, and you always have to ask yourself that,” Madry said. “You should never treat this as a black box, that just comes as an oracle … yes, you should use it, but then try to get a feeling of what are the rules of thumb that it came up with? And then validate them.” Related Articles. What business leaders need to know about AI 7 lessons for successful machine learning projects Why finance is deploying natural language processing This is especially important because systems can be fooled and undermined, or just fail on certain tasks, even those humans can perform easily. For example, adjusting the metadata in images can confuse computers — with a few adjustments, a machine identifies a picture of a dog as an ostrich. Madry pointed out another example in which a machine learning algorithm examining X-rays seemed to outperform physicians. But it turned out the algorithm was correlating results with the machines that took the image, not necessarily the image itself. Tuberculosis is more common in developing countries, which tend to have older machines. The machine learning program learned that if the X-ray was taken on an older machine, the patient was more likely to have tuberculosis. It completed the task, but not in the way the programmers intended or would find useful. The importance of explaining how a model is working — and its accuracy — can vary depending on how it’s being used, Shulman said. While most well-posed problems can be solved through machine learning, he said, people should assume right now that the models only perform to about 95% of human accuracy. It might be okay with the programmer and the viewer if an algorithm recommending movies is 95% accurate, but that level of accuracy wouldn’t be enough for a self-driving vehicle or a program designed to find serious flaws in machinery.    Bias and unintended outcomes Machines are trained by humans, and human biases can be incorporated into algorithms — if biased information, or data that reflects existing inequities, is fed to a machine learning program, the program will learn to replicate it and perpetuate forms of discrimination. Chatbots trained on how people converse on Twitter can pick up on offensive and racist language, for example. In some cases, machine learning models create or exacerbate social problems. For example, Facebook has used machine learning as a tool to show users ads and content that will interest and engage them — which has led to models showing people extreme content that leads to polarization and the spread of conspiracy theories when people are shown incendiary, partisan, or inaccurate content. Ways to fight against bias in machine learning including carefully vetting training data and putting organizational support behind ethical artificial intelligence efforts, like making sure your organization embraces human-centered AI, the practice of seeking input from people of different backgrounds, experiences, and lifestyles when designing AI systems. Initiatives working on this issue include the Algorithmic Justice League and The Moral Machine project. Putting machine learning to work. Shulman said executives tend to struggle with understanding where machine learning can actually add value to their company. What’s gimmicky for one company is core to another, and businesses should avoid trends and find business use cases that work for them. The way machine learning works for Amazon is probably not going to translate at a car company, Shulman said — while Amazon has found success with voice assistants and voice-operated speakers, that doesn’t mean car companies should prioritize adding speakers to cars. More likely, he said, the car company might find a way to use machine learning on the factory line that saves or makes a great deal of money. “The field is moving so quickly, and that's awesome, but it makes it hard for executives to make decisions about it and to decide how much resourcing to pour into it,” Shulman said. It’s also best to avoid looking at machine learning as a solution in search of a problem, Shulman said. Some companies might end up trying to backport machine learning into a business use. Instead of starting with a focus on technology, businesses should start with a focus on a business problem or customer need that could be met with machine learning.  A basic understanding of machine learning is important, LaRovere said, but finding the right machine learning use ultimately rests on people with different expertise working together. “I'm not a data scientist. I'm not doing the actual data engineering work — all the data acquisition, processing, and wrangling to enable machine learning applications — but I understand it well enough to be able to work with those teams to get the answers we need and have the impact we need,” she said. “You really have to work in a team.” Learn more:  . Sign-up for a Machine Learning in Business Course. Watch an Introduction to Machine Learning through MIT OpenCourseWare. Read about how an AI pioneer thinks companies can use machine learning to transform. Watch a discussion with two AI experts about machine learning strides and limitations. Take a look at the seven steps of machine learning. Read next: 7 lessons for successful machine learning projects  For more info Sara Brown News Writer [email protected] Related Articles. Ideas Made to Matter How chief financial officers are optimizing their KPIs Ideas Made to Matter 2021, illustrated Ideas Made to Matter The top 10 MIT Sloan news stories of 2021
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                            [body] => Learning Path : Your mentor to become a machine learning expert Looking to learn machine learning in 2020? Here’s the updated comprehensive and structured learning path to master the domain this year! Machine learning is a complex topic to master! Not only there is a plethora of resources available, they also age very fast. Couple this with a lot of technical jargon and you can see why people get lost while pursuing machine learning. However, this is only part of the story. You can not master machine learning with out undergoing the grind yourself. You have to spend hours understanding the nuances of feature engineering, its importance and the impact it can have on your models. Through this learning path, we hope to provide you an answer to this problem. We have deliberately loaded this learning path with a lot of practical projects. You can not master machine learning with the hard work! But once you do, you are one of the highly sought after people around. Since this is a complex topic, we recommend you to strictly follow the steps in sequential order. Consider this as your mentor for machine learning. Only skip a step, if you know the subject matter mentioned in that step already.   Warming up – how is machine learning useful? If you are a complete starter to machine learning, here is a good talk from Jeremy Howard to understand how machine learning is changing this world. Jeremy discusses various applications of machine learning and deep learning. Jeremy, also discusses a few ways in which machine learning can impact this world. Still not sure, check out this smaller video on training a machine to play Super Mario. Excited about what machine learning can achieve? Let’s look at a learning path to make you a machine learning expert. Optional read: Basics of Machine learning for a newbie   Step 0: Basics of R / Python. There are multiple languages which provide machine learning capabilities. Also, there is development work happening at a rapid pace across several languages. Currently “R” and “Python” are the most commonly used languages and there is enough support / community available for both. Before entering into world of ML, I would recommend you to choose one of these two language (R or Python) which can help to focus on machine learning (Which is better – R or Python?). Keep your focus on understanding the basics of the language, libraries and data structure. Here’s the step by step guide to learn R and Python: a) Learning Path on R: Step 0 to Step 2 b) Learning Path on Python: Step 0 to Step 2 Other languages you can consider: Scala, Go / Julia in coming time   Step 1: Learn basic Descriptive and Inferential Statistics. Let’s start or refresh our statistical learning. It is good to have understanding about the descriptive and inferential statistics before you start serious machine learning development. Udacity offers course on descriptive statistics and Inferential statistics. Both courses would make use of Excel to teach you all the basics of statistics. If you already know them, you can refresh or skip this step. Assignment: You can perform assignments of both the courses using your choice of language (R / Python). You can refer respective statistical libraries and methods for both the languages below. R: Stats Python: Scipy, Numpy, Pandas Must read: Difference between machine learning and statistical modeling?   Step 2: Data Exploration / Cleaning / Preparation. What differentiates a good machine learning professional from an average one is the quality of feature engineering and data cleaning which happens on the original data. The more quality time you spend here, the better it is. This step also takes the bulk of your time and hence it helps to put a structure around it. You can refer series of articles below to learn different stages of data explorations. Variable Identification, Univariate and Multivariate analysis Missing values treatment Outlier treatment Feature Engineering You can also refer Data exploration methods in R and Python: Data Exploration in R Data Exploration in Python Exercise / assignment:. Take up the titanic survival problem from Kaggle, build a set of hypothesis and then clean the data, add new features to the existing dataset. Think what is the best way to impute missing age? Similarly, take up the Bike sharing demand forecasting problem and repeat the cycle mentioned above.   Step 3: Introduction to Machine Learning. You should now open the doors for Machine Learning. There are various resources available to start with Machine learning techniques. I would suggest you to pick one of the following 2 ways depending on your style of learning: Option 1: If you are some one who likes to take learning in small small steps and need more hand holding, you should start from Machine learning course from Andrew Ng: It is a good course for beginners and easy to understand. Professor Ng is amazing in making difficult concepts come to you so smoothly. The course covers all the basic algorithms and also introduces a few advanced topics like neural networks, Recommendation system and application of machine learning in large databases using Map Reduce. He chooses to use Octave / MATLAB instead of the more popular R or Python for teaching machine learning. Once completed, you should proceed to exercises and homework provided in Option 2. Option 2: If you are more independent, like challenges and can battle out tough assignments, you should take Learning form Data course by Prof. Yaser Abu-Mostafa: This course gives an amazing treatment of the concepts behind machine learning but beware this course is quite heavy on math and the theory behind ML (stuff like the VC dimension). It also requires more programming knowledge and is thus more advanced in that sense. This course is loaded with home works (which is not necessarily a bad thing ). Now, you have good understanding about the algorithms and techniques. Let’s look at the libraries or packages available in R or Python. You can refer learning path (step-6 ) of R (additionally, ML Algorithms in R) and Python to explore about these packages and related options.   Step 4: Participate in Kaggle Knowledge competition. By now, you have all the tools you need to compete on Kaggle knowledge competitions. These knowledge competitions have less difficulty level as compared to prize winning challenges. You can also find various related resources to kick start your data science journey. Below are the list of currently active knowledge competition: Titanic: Machine Learning from Disaster San Francisco Crime Classification Must Read: How do I start my journey on Kaggle?   Step 5: Advanced Machine Learning. Now that you have learnt most of machine learning techniques, it’s time to explore advanced machine learning techniques to understand different structure of data like Deep Learning and Machine Learning with Big Data. Deep Learning. Are you aware about deep learning? if not, here is a brief introduction about it and more detail on deep learning watch video here. Below are the list of deep learning resources that will help you to get started: The most comprehensive resource is deeplearning.net. You will find everything here – lectures, datasets, challenges, tutorials. Another course from Geoff Hinton a try in a bid to understand the basics of Neural Networks Pattern recognition using Python (Resource 1, Resource 2, Resource 3) and R (Resource 1) Text Mining using Python (Resource) and R (Resource 1 , Resource 2)   Ensemble modeling. This is where an expert is different from an average professional. Ensembling can add a lot of power to your models and has been a very successful technique in various Kaggle competitions. Here is one of the best guide on emsemble modeling we have come across.   Machine Learning with Big Data. As you know that the size of data is increasing at an exponential rate but raw data is not useful till you start getting insights from it. Machine learning is nothing but learning from data, generate insight  or identifying pattern in the available data set. There are various application of machine learning algorithms like “spam detection”, “web document classification”, “fraud detection”, “recommendation system” and many others.  Below are the list of tutorials to deal with big data using machine learning. Scalable Machine Learning Packages for Big Data in Python ( Pydoop, PyMongo) and R (Resource1, Resource2)   Step 6: Participate in main stream Kaggle Competition. Now you have most of the technical and statistical skills. It’s time to start learning from fellow data scientists while competing with them. Kaggle  is a similar place as what we want a more active, engaged and competitive platform. Data scientists are passionate about their rank and model performance. Go, dive into one of the live competitions currently running on Kaggle and give all what you have learnt a try! Good luck!   Optional step: Text mining and databases. If you need to apply machine learning to text mining, you can look at the following guide to clean text data and build models on it. You can also look at the following Kaggle competition: De-noising dirty documents Sentiment analysis on movie reviews   The Fun part. Now that you know what and where to learn to become a machine learning professional, here is a small simulation of how a genetic algorithm based robot would learn walking   And some serious stuff. Now that you know the potential of machine learning, imagine the impact it could have on today’s world. The talk from Jeremy mentions briefly about this. Following article tells about this evolution from a different perspective: part 1 & part 2    Hope you enjoyed this learning path on machine learning and the impact machine learning can have on our future. If you have any suggestions to improve this learning path, please feel free to share them through comments below.   If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page. We use cookies on Analytics Vidhya websites to deliver our services, analyze web traffic, and improve your experience on the site. By using Analytics Vidhya, you agree to our Privacy Policy and Terms of Use.AcceptPrivacy & Cookies Policy Close Privacy Overview. This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience. Necessary Necessary Always Enabled Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information. Non-necessary Non-necessary Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website. SAVE & ACCEPT
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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] => Machine LearningFilled StarFilled StarFilled StarFilled StarFilled Star4.9stars166,697 ratings•42,681 reviewsAndrew 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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                            [body] => How to Learn Machine Learning The Self-Starter Way. Hello, and welcome! In this guide, we're going to reveal how you can get a world-class machine learning education for free. You don't need a fancy Ph.D in math. You don't need to be the world's best programmer. And you certainly don't need to pay $16,000 for an expensive "bootcamp." Whether your goal is to become a data scientist, use ML algorithms as a developer, or add cutting-edge skills to your business analysis toolbox, you can pick up applied machine learning skills much faster than you might think. 1. Are you a self-starter? Do you like to learn with hands-on projects? Are you driven and self-motivated? Can you commit to goals and see them through? If so, you'll love studying machine learning. You'll get to solve interesting challenges, tinker with fascinating algorithms, and build an incredibly valuable career skill. 2. Are you tired of seeing expensive courses and bootcamps? We are too... That's why we put together this guide of completely free resources anyone can use to learn machine learning. The truth is that most paid courses out there recycle the same content that's already available online for free. We'll pull back the curtains and reveal where to find them for yourself. 3. Do you want a single page on the internet that will always be up-to-date? Machine learning is a rapidly evolving field. That makes it exciting to learn, but materials can become outdated quickly. We're going to update this page regularly with the best resources to learn machine learning. We've got a lot of great stuff you'll like, so let's dive right in! This is exciting stuff! Table of Contents. . Intro to Machine Learning WTF is Machine Learning? Why Learn Machine Learning? The Self-Starter Way Free Self-Study ML Course Step 0: Prerequisites Step 1: Sponge Mode Step 2: Targeted Practice Step 3: Machine Learning Projects Bonus Goodies Top 10 Tips for Beginners More Resources The Accelerated Self-Starter Way Introduction to Machine Learning:. WTF is Machine Learning? Machine Badass (NOT Machine Learning) Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to. It sits at the intersection of statistics and computer science, yet it can wear many different masks. You may also hear it labeled several other names or buzz words: Data Science, Big Data, Artificial Intelligence, Predictive Analytics, Computational Statistics, Data Mining, Etc... While machine learning does heavily overlap with those fields, it shouldn't be crudely lumped together with them. For example, machine learning is one tool for data science (albeit an essential one). It's also one use of infrastructure that can handle big data. Here are some examples: Supervised Learning - Your email provider kindly places that sketchy email from the "Nigerian prince with $50,000 to deposit into an overseas bank account" into the spam folder. Unsupervised Learning - Marketing firms "kindly" use hundreds of behavior and demographic indicators to segment customers into targeted offer groups. Reinforcement Learning - A computer and camera within a self-driving car interact with the road and other cars to learn how to navigate a city. Don't worry if some of those terms mean nothing to you. After you complete this guide, you'll be able to apply each of those techniques yourself! (Self-driving car not included.) Self-driving car: NOT included in this guide! Back to Table of Contents Why Learn Machine Learning? Have you ever wanted to take over the world with robot raccoons?... Or program your own personal butler like J.A.R.V.I.S. from Iron Man?!... Or crack the stock market and become a billionaire overnight??!!... Well, sorry to be a party pooper... but you probably won't be able to do that with machine learning (yet). But there are still awesome reasons to learn machine learning! Here are a few: Massive Global Demand. The demand for machine learning is booming all over the world. Entry salaries start from $100k – $150k. Data scientists, software engineers, and business analysts all benefit by knowing machine learning. Data is Power. Data is transforming everything we do. All organizations, from startups to tech giants to Fortune 500 corporations, are racing to harness their data. Big and small data will continue to reshape technology and business. It's Fun as Hell!OK, we may be a bit biased, but ML is really damn cool. It has a unique blend of discovery, engineering, and business application that makes it one-of-a-kind. You’ll have a ton of fun with this rich and vibrant field. Back to Table of Contents The Self-Starter Way. The self-starter way of mastering ML is to learn by "doing shit." (not the technical term). Traditionally, students will first spend months or even years on the theory and mathematics behind machine learning. They'll get frustrated by the arcane symbols and formulas or get discouraged by the sheer volume of textbooks and academic papers to read. Unless you want to devote yourself to Ph.D research, that's way overkill. For most people, the self-starter approach is superior to the academic approach for 3 reasons: You'll have more fun. By cycling between theory, practice, and projects, you'll arrive at real results faster. This is a huge boost in morale. You'll build practical skills the industry demands. Businesses don't care if you can derive proofs. They care if you can turn their data into gold. You'll build your portfolio along the way. With hands-on projects, you'll conveniently build a portfolio you can show employers. In a nutshell, the self-starter way is faster and more practical. However, it definitely puts more responsibility in your own hands to follow through. Hopefully this guide will help you stay on track! Here are the 4 steps to learning machine through self-study: 0 Prerequisites Build a foundation of statistics, programming, and a bit of math. 1 Sponge Mode Immerse yourself in the essential theory behind ML. 2 Targeted Practice Use ML packages to practice the 9 essential topics. 3 Machine Learning Projects Dive deeper into interesting domains with larger projects. Back to Table of Contents Free Self-Study Machine Learning Course:. Step 0: Prerequisites. Machine learning can appear intimidating without a gentle introduction to its prerequisites. You don't need to be a professional mathematician or veteran programmer to learn machine learning, but you do need to have the core skills in those domains. The good news is that once you fulfill the prerequisites, the rest will be fairly easy. In fact, almost all of ML is about applying concepts from statistics and computer science to data. Task: Make sure you are caught up to speed for at least programming and statistics. Python for Data Science. You can’t use machine learning unless you know how to program. Luckily, we have a free guide: How to Learn Python for Data Science, The Self-Starter Way Statistics for Data Science. Understanding statistics, especially Bayesian probability, is essential for many machine learning algorithms. We have a free guide for you: How to Learn Statistics for Data Science, The Self-Starter Way Math for Data Science. Original algorithm research requires a foundation in linear algebra and multivariable calculus. We have a free guide: How to Learn Math for Data Science, The Self-Starter Way Back to Table of Contents Step 1: Sponge Mode. Sponge mode is all about soaking in as much theory and knowledge as possible to give yourself a strong foundation. Pictured: Spongebob (NOT Sponge Mode) Now, some people may be wondering: "If I don't plan to perform original research, why would I need to learn the theory when I can just use existing ML packages?" This is a reasonable question! However, learning the fundamentals is important for anyone who plans to apply machine learning in their work. Here are 5 super practical reasons for learning ML theory. They span the entire modeling process: Planning and data collection. Data collection can be an expensive and time consuming process. What types of data do I need to collect? How much data do I need (hint: it's different depending on the model)? Is this challenge feasible? Data assumptions and preprocessing. Different algorithms have different assumptions about the input data. How should I preprocess my data? Should I normalize it? Is my model robust to missing data? How about outliers? Interpreting model results. The notion that ML is a "black box" is simply false. Yes, not all results are directly interpretable, but you need to be able to diagnose your models to improve them. How can I tell if my model is overfit or underfit? How do I explain these results to business stakeholders? How much room for improvement is left? Improving and tuning your models. You'll rarely reach the best model on your first try. You need to understand the nuances of different tuning parameters and regularization methods. If my model is overfit, how can I remedy it? Should I spend more time on feature-engineering or on data collection? Can I ensemble my models?  Driving to business value. ML is never done in a vacuum. If you don't truly understand the tools in your arsenal, you can't maximize their effectiveness. Which outcome metrics are most important to optimize? Are there other algorithms that work better here? When is ML not the answer? Here's the great news... you don't need to have all the answers to these questions right from the start. In fact, the approach we recommend is to learn just enough theory to get started and not go astray. Then, you can build mastery over time by alternating between theory and practice. 1.1 Best Free Machine Learning Courses. These next two free courses are world-class (from Harvard and Stanford) resources for Sponge Mode. Task: Complete at least one of the courses below. Harvard's Data Science Course. End-to-end data science course. While there’s less  emphasis on ML than in Andrew Ng’s course, you’ll get more practice with the entire data science workflow from data collection to analysis. (Course Homepage | Lecture Videos and Slides | Homework Assignments) Stanford's Machine Learning Course. This is the famous course taught by Andrew Ng, and it’s the gold standard when it comes to learning machine learning theory. These videos really clear up the core concepts behind ML. If you only have time for 1 course, we recommend this one. (Course Videos) 1.2 Keys to Success. Here are a few keys to success for this step: A.) Pay attention to the big picture and always ask "why." Every time you're introduced to a new concept, ask "why." Why use a decision tree instead of regression in some cases? Why regularize parameters? Why split your dataset? When you understand why each tool is used, you'll become a true machine learning practitioner. For example, by the end of this step, you should know when to preprocess your data, when to use supervised vs. unsupervised algorithms, and methods for preventing model overfitting. B.) Accept that you will not remember everything. Don't stress about taking insane notes or reviewing everything 3 times. Accept that you'll need to cycle back and review concepts as you encounter them in the wild. C.) Keep moving and don't be discouraged. Try to avoid dwelling on any topic for too long. Some concepts can't be explained easily, even by the best professors. Your confusion will clear up once you start applying them in practice. D.) Videos are more effective than textbooks. From our experience, textbooks can be great reference tools, but they often omit the vital color commentary surrounding key concepts. We strongly recommend video lectures during Sponge Mode. 1.3 Free Reference Textbooks. Next, we have free (legal) PDFs of 2 classic textbooks in the industry. Task: Download the free PDFs for your future reference. An Introduction to Statistical Learning. Gentler introduction than Elements of Statistical Learning. Recommended for everyone. (PDF) Elements of Statistical Learning. Rigorous treatment of ML theory and mathematics. Recommended for ML researchers. (PDF) Back to Table of Contents Step 2: Targeted Practice. After Sponge Mode, you've probably already gotten a healthy dose of practice. Now it's time to take that practice to the next level. Step 2: Targeted Practice is all about using specific, deliberate exercises to hone your skills. The goal of this step is threefold: Practice the entire machine learning workflow: Data collection, cleaning, and preprocessing. Model building, tuning, and evaluation. Practice on real datasets: You'll start to build intuition around which types of models are appropriate for which types challenges. Deep dive on individual topics: For example, in Step 1, you learned about clustering algorithms. In Step 2, you'll apply different types of clustering algorithms on datasets to see which perform the best. After this step, you'll be ready to tackle bigger projects without feeling overwhelmed. 2.1 - The 9 Essential Topics. Machine learning is a broad and rich field. There are applications for almost any industry. It's easy to get flustered by all there is to learn. Plus, it's also easy to get lost in the weeds of individual models and lose sight of the big picture. Therefore, we've broken the essentials into the following 9 topics. These are building block topics that collectively represent the simple value proposition of machine learning: taking data and transforming it into something useful. The Big Picture. Essential ML theory, such as the Bias-Variance tradeoff. Optimization. Algorithms for finding the best parameters for a model. Data Preprocessing. Dealing with missing data, skewed distributions, outliers, etc. Sampling & Splitting. How to split your datasets to tune parameters and avoid overfitting. Supervised Learning. Learning from labeled data using classification and regression models. Unsupervised Learning. Learning from unlabeled data using factor and cluster analysis models. Model Evaluation. Making decisions based on various performance metrics. Ensemble Learning. Combining multiple models for better performance. Business Applications. How machine learning can help different types of businesses. 2.2 - Tools of the Trade. For this step, we strongly recommend that you start with out-of-the-box algorithm implementations for two reasons. First, this is how most ML is performed in the industry. Sure, there will be times when you'll need to research original algorithms or develop them from scratch, but prototyping always starts with existing libraries. Second, you'll get the chance to practice the entire ML workflow without spending too much time on any one portion of it. This will give you an invaluable "big picture intuition." Depending on your programming language of choice, you have 2 excellent options. Task: Complete the Quickstart guide for one of the libraries below. Python: Scikit-Learn. Scikit-learn, or sklearn, is the gold standard Python library for general purpose machine learning. It does almost everything, and it has implementations of all the common algorithms. Scikit-Learn Tutorial, Wine Snob Edition R: Caret. Caret is love. Caret is life. Caret is a library that provides a unified interface for many different model packages in R. It also includes functions for preprocessing, data splitting, and model evaluation, making it a complete end-to-end solution. Quickstart Webinar 2.3 - Datasets for Practice. For this step, you'll need datasets to practice building and tuning models. Again, the point of Step 2: Targeted Practice is to take the theory that's floating around in your mind after Step 1: Sponge Mode and put it into code. Much of the art in data science and machine learning lies in dozens of micro-decisions you'll make to solve each problem. This is the perfect time to practice making those micro-decisions and evaluating the consequences of each. Task: Pick 5-10 datasets from the options below. We recommend starting with the UCI Machine Learning Repository. For example, you can pick 3 datasets each for regression, classification, and clustering. Task: For each dataset, try at least 3 different modeling approaches using Scikit-Learn or Caret. Think about the following questions: What types of preprocessing do you need to perform for each dataset? Do you need to reduce dimensions or perform feature selection? If so, what methods can you use? How should you sample or split your dataset? How do you know if your model is overfit? What types of performance metrics should you use? How do different tuning parameters affect your model results? Can you ensemble to get better results? (For clustering) Do your clusters appear intuitive? We also have a curated list of some of our favorite datasets for practice and projects. UCI Machine Learning Repo. This is an incredible collection of over 350 different datasets specifically curated for practicing machine learning. You can search by task (i.e. regression, classification, or clustering), industry, dataset size, and more. (Go to website) Kaggle. Kaggle.com is most famous for hosting data science competitions, but the site also houses over 180 community datasets for fun topics ranging from Pokemon data to European Soccer matches. (Go to website) Data.gov. If you’re looking for social science or government-related datasets, look no further than Data.gov, a collection of the U.S. government’s open data. You can search over 190,000 datasets. (Go to website) Back to Table of Contents Step 3: Machine Learning Projects. Alright, now comes the really fun part! Up to now, we've covered prerequisites, essential theory, and targeted practice. We're now ready to dive into some bigger projects. The goal of this step is to practice integrating machine learning techniques into complete, end-to-end analyses. Task: Complete the projects below. The order is up to you, but we ordered them by difficulty (easiest first). 3.1 - Titanic Survivor Prediction. The Titanic Survivor Prediction challenge is an incredibly popular project for practicing machine learning. In fact, it's the most popular competition on Kaggle.com. We love this project as a starting point because there's a wealth of great tutorials out there. You can take a peek into the minds of more experienced data scientists and see how they approach data exploration, feature engineering, and model tuning. The Titanic is sinking! Python Tutorials Four-Part Tutorial by Kaggle - Detailed tutorial that starts from cleaning and exploring the data. We really like this tutorial because it teaches you how to properly preprocess and wrangle your data properly before using sklearn. Tutorial and iPython Notebooks by Pycon UK - Great tutorial that's presented in iPython Notebook. It has excellent appendices on cross-validation and visualization. R Tutorials Binary Outcome Modeling Tutorial - Walks through a couple different models in R using the caret package. This tutorial nicely summarizes the predictive modeling process from end-to-end. An "Irresponsibly" Fast Tutorial - Bare bones tutorial that completely skips the theory. Useful as another perspective (and it shows random forests in action). 3.2 - Algorithm from Scratch. There's nothing that pushes your understanding quite like writing an algorithm from scratch. They say the devil's in the details, and here's where that really rings true. We recommend starting with something simple, like logistic regression, decision trees, or k-nearest neighbors. This project will also give you invaluable practice in translating math into code. This skill will be very handy when you eventually need to use the latest research from academia in your work. If you get stuck, here are some tips: Wikipedia is a great resource for this project because it has pseudo-code for many common algorithms. For inspiration, try looking at the source code from existing ML packages. Break your algorithm into pieces. Write separate functions for sampling, gradient descent, etc. Start simple. Implement a decision tree before trying to write a random forest. She's only a few years away from learning machine learning... 3.3 - Pick a Fun Project or Interesting Domain. You wouldn't be a self-starter if you didn't have curiosity and ideas. By now, you're probably itching to get started (or have already started) on some grand idea that you've been mulling over. This is honestly the best part about learning machine learning. It's such a powerful tool that once you start to understand, so many ideas will come to you. The good news is that if you've been following along, then you're more than ready to jump in. Go forth, and reap the fruits of your labor! We'll also keep a list of project ideas here for inspiration: Project Ideas 8 Fun Machine Learning Projects for Beginners Back to Table of Contents Great Job! (So Far...). Congratulations on reaching the end of the self-study guide! Here's some great news: If you've followed along and completed all the tasks, you're better at applied machine learning than 90% of the people out there claiming to be data scientists. You have an awesome skillset that employers will drool over. Now, here's some better news: There's still much to learn! For example, deep learning, computer vision, and natural language processing are a few of the fascinating, cutting-edge subfields that await you. The key to becoming the best data scientist or machine learning engineer you can be is to never stop learning. Welcome to the start of your journey in this dynamic, exciting field! So great job! So far... Back to Table of Contents Bonus Goodies:. Top 10 Tips for Beginners. If you've chosen to seriously study machine learning, then congratulations! You have a fun and rewarding journey ahead of you. Here are 10 tips that every beginner should know: 1. Set concrete goals or deadlines. Machine learning is a rich field that's expanding every year. It can be easy to go down rabbit holes. Set concrete goals for yourself and keep moving. 2. Walk before you run. You might be tempted to jump into some of the newest, cutting edge sub-fields in machine learning such as deep learning or NLP. Try to stay focused on the core concepts at the start. These advanced topics will be much easier to understand once you've mastered the core skills. 3. Alternate between practice and theory. Practice and theory go hand-in-hand. You won't be able to master theory without applying it, yet you won't know what to do without the theory. 4. Write a few algorithms from scratch. Once you've had some practice applying algorithms from existing packages, you'll want to write a few from scratch. This will take your understanding to the next level and allow you to customize them in the future. 5. Seek different perspectives. The way a statistician explains an algorithm will be different from the way a computer scientist explains it. Seek different explanations of the same topic. 6. Tie each algorithm to value. For each tool or algorithm you learn, try to think of ways it could be applied in business or technology. This is essential for learning how to "think" like a data scientist. 7. Don't believe the hype. Machine learning is not what the movies portray as artificial intelligence. It's a powerful tool, but you should approach problems with rationality and an open mind. ML should just be one tool in your arsenal! 8. Ignore the show-offs. Sometimes you'll see people online debating with lots of math and jargon. If you don't understand it, don't be discouraged. What matters is: Can you use ML to add value in some way? And the answer is yes, you absolutely can. 9. Think "inputs/outputs" and ask "why." At times, you might find yourself lost in the weeds. When in doubt, take a step back and think about how data inputs and outputs piece together. Ask "why" at each part of the process. 10. Find fun projects that interest you! Rome wasn't built in a day, and neither will your machine learning skills be. Pick topics that interest you, take your time, and have fun along the way. Back to Table of Contents More Resources. We'll be keeping this section updated with the best additional resources for learning machine learning, so keep this page bookmarked (links here open in a new tab). Other posts you may like: 21 Must-Know Machine Learning Interview Questions & Answers 5 Tasty Python Web Scraping Libraries 5 Heroic Python NLP Libraries 5 Genius Python Deep Learning Libraries Awesome Machine Learning TED Talks: Jeremy Howard: The wonderful and terrifying implications of computers that can learn Blaise Agüera y Arcas: How computers are learning to be creative Anthony Goldbloom: The jobs we'll lose to machines — and the ones we won't ML Innovation and Entrepreneurship Shivon Zilis: The Current State of Machine Intelligence Back to Table of Contents Copyright 2016-2020 - EliteDataScience.com - All Rights Reserved
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                            [description] => Data Science vs Machine Learning: Know the exact differences between Data Science, AI & ML - along with their definitions, nature, scope, and careers. Read this full post to know more
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                                            [question] => Are Machine Learning and Data Science the same?
                                            [answer] => No, Machine Learning and Data Science are not the same. They are two different domains of technology that work on two different aspects of businesses around the world. While Machine Learning focuses on enabling machines to self-learn and execute any task, Data science focuses on using data to help businesses analyse and understand trends. However, that’s not to say that there isn’t any overlap between the two domains. Both Machine Learning and Data Science depend on each other for various kinds of applications as data is indispensable and ML technologies are fast becoming an integral part of most industries.
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                                            [answer] => Since both Machine Learning and Data Science are closely connected, a basic knowledge of each is required to specialise in either of the two domains. Having said that, more than data science the knowledge of data analysis is required to get started with Machine Learning. Learning programming languages like R, Python and Java are required to understand and clean data to use it for creating ML algorithms. Most Machine Learning courses include tutorials on these programming languages and basic data analysis and data science concepts.
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                                            [answer] => Putting it slightly differently – Data Science is the future. No businesses or industries for that matter will be able to keep up without data science. A large number of transitions have already happened worldwide where businesses are seeking more data-driven decisions, more is to follow suit. Data science quite rightly has been dubbed as the oil of the 21st century which can mean endless possibilities across industries. So, if you are keen on pursuing this path, your efforts will be highly rewarded with not just a fulfilling career and fat pay cheques but also a lot of job security.
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                                            [answer] => Yes, Data Scientists can become Machine Learning. In fact, it will not be very difficult for data scientists to transition to a Machine Learning career since they would have anyway worked closely on Data Science technologies that are frequently used in Machine Learning. Machine Learning languages, libraries and more are often used in data science applications as well. So data science professionals do not need to put in a humongous amount of effort to make this transition. So yes, with the right kind of upskilling course, data scientists can become machine learning engineers.
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                            [body] => Data Science vs Machine Learning and Artificial Intelligence By Marina Chatterjee - Jan 11, 2020 113387 0 Share Facebook Twitter WhatsApp Attend our Upcoming Live Webinar on Artificial Intelligence and Machine Learning Register now for FREE What is Data Science?What is Artificial Intelligence?What is Machine Learning?Difference between AI and Machine Learning Difference between machine learning and data scienceRelationship Between Data Science, Artificial Intelligence and Machine LearningDifference Between Data Science, Artificial Intelligence and Machine Learning Machine Learning vs Data Science Salary Data Science, Artificial Intelligence and Machine Learning JobsFAQs– Are Machine Learning and Data Science the same?– Which is better, Machine Learning or Data Science?– Is Data Science required for Machine Learning?– Who earns more, Data Scientist or Machine Learning Engineer?– What is the Future of Data Science?– Can a Data Scientist become a Machine Learning Engineer? While the terms Data Science, Artificial Intelligence (AI) and Machine learning fall in the same domain and are connected to each other, they have their specific applications and meaning. There may be overlaps in these domains every now and then, but essentially, each of these three terms has unique uses of its own.  Our Most Popular Free Courses: > < Beginner 1.0 Hours Introduction to Artificial Intelligence ★ 4.47 (200 Ratings) Free Enrol Now → Beginner 2.5 Hours Basics of Machine Learning ★ 4.34 (1506 Ratings) Free Enrol Now → Beginner 2.0 Hours Data Science Foundations ★ 4.43 (3282 Ratings) Free Enrol Now → Here is a brief about Data Science vs Machine Learning vs AI in a shorter video version. What is Data Science? You must have wondered, ‘What is Data Science?’, Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. Data scientists use a combination of tools, applications, principles and algorithms to make sense of random data clusters. Since almost all kinds of organizations today are generating exponential amounts of data around the world, it becomes difficult to monitor and store this data. Data science focuses on data modelling and data warehousing to track the ever-growing data set. The information extracted through data science applications are used to guide business processes and reach organisational goals. Scope of Data Science. One of the domains that data science influences directly is business intelligence. Having said that, there are functions that are specific to each of these roles. Data scientists primarily deal with huge chunks of data to analyse the patterns, trends and more. These analysis applications formulate reports which are finally helpful in drawing inferences. A Business Intelligence expert picks up where a data scientist leaves – using data science reports to understand the data trends in any particular business field and presenting business forecasts and course of action based on these inferences. Interestingly, there’s also a related field which uses both data science, data analytics and business intelligence applications- Business Analyst. A business analyst profile combines a little bit of both to help companies take data driven decisions.   Data scientists analyse historical data according to various requirements, by applying different formats, namely: Predictive causal analytics: Data scientists use this model to derive business forecasts. The predictive model showcases the outcomes of various business actions in measurable terms. This can be an effective model for businesses trying to understand the future of any new business move.  Prescriptive Analysis: This kind of analysis helps businesses set their goals by prescribing the actions which are most likely to succeed. Prescriptive analysis uses the inferences from the predictive model and helps businesses by suggesting the best ways to achieve those goals. Data science uses a wide array of data-oriented technologies including SQL, Python, R, and Hadoop, etc. However, it also makes extensive use of statistical analysis, data visualization, distributed architecture, and more to extract meaning out of sets of data. Data scientists are skilled professionals whose expertise allows them to quickly switch roles at any point in the life cycle of data science projects. They can work with Artificial Intelligence and machine learning with equal ease. In fact, data scientists need machine learning skills for specific requirements like: Machine Learning for Predictive Reporting: Data scientists use machine learning algorithms to study transactional data to make valuable predictions. Also known as supervised learning, this model can be implemented to suggest the most effective courses of action for any company. Machine Learning for Pattern Discovery: Pattern discovery is important for businesses to set parameters in various data reports and the way to do that is through machine learning. This is basically unsupervised learning where there are no pre-decided parameters. The most popular algorithm used for pattern discovery is Clustering. Read Also: Artificial Intelligence and The Human Mind: When will they meet?  What is Artificial Intelligence? AI, a rather hackneyed tech term that is used frequently in our popular culture – has come to be associated only with futuristic-looking robots and a machine-dominated world. However, in reality, Artificial Intelligence is far from that. Simply put, artificial intelligence aims at enabling machines to execute reasoning by replicating human intelligence. Since the main objective of AI processes is to teach machines from experience, feeding the right information and self-correction is crucial. AI experts rely on deep learning and natural language processing to help machines identify patterns and inferences. Scope of Artificial Intelligence. Automation is easy with AI: AI allows you to automate repetitive, high volume tasks by setting up reliable systems that run frequent applications.Intelligent Products: AI can turn conventional products into smart commodities. AI applications when paired with conversational platforms, bots and other smart machines can result in improved technologies.Progressive Learning: AI algorithms can train machines to perform any desired functions. The algorithms work as predictors and classifiers.Analysing Data: Since machines learn from the data we feed them, analysing and identifying the right set of data becomes very important. Neural networking makes it easier to train machines. What is Machine Learning? Machine Learning is a subsection of Artificial intelligence that devices means by which systems can automatically learn and improve from experience. This particular wing of AI aims at equipping machines with independent learning techniques so that they don’t have to be programmed to do so, this is the difference between AI and Machine Learning. Enroll to Machine Learning Course For Free Machine learning involves observing and studying data or experiences to identify patterns and set up a reasoning system based on the findings. The various components of machine learning include: Supervised machine learning: This model uses historical data to understand behaviour and formulate future forecasts. This kind of learning algorithms analyse any given training data set to draw inferences which can be applied to output values. Supervised learning parameters are crucial in mapping the input-output pair. Unsupervised machine learning: This type of ML algorithm does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly. Algorithms with unsupervised learning can use both generative learning models and a retrieval-based approach. Semi-supervised machine learning: This model combines elements of supervised and unsupervised learning yet isn’t either of them. It works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive. Reinforcement machine learning: This kind of learning doesn’t use any answer key to guide the execution of any function. The lack of training data results in learning from experience. The process of trial and error finally leads to long-term rewards. Machine learning delivers accurate results derived through the analysis of massive data sets. Applying AI cognitive technologies to ML systems can result in the effective processing of data and information. But what are the key differences between Data Science vs Machine Learning and AI vs ML? Continue reading to learn more. Difference between AI and Machine Learning . Artificial IntelligenceMachine LearningAI aims to make a smart computer system work just like humans to solve complex problemsML allows machines to learn from data so they can provide accurate outputBased on capability, AI can be categorized into Weak AI, General AI, and Strong AIML can be categorized into Supervised Learning, Unsupervised Learning, and Reinforcement LearningAI systems are concerned with maximizing the chances of successMachine Learning primarily concerns with accuracy and patternsAI enables a machine to emulate human behaviorMachine Learning is a sub-set of AIMainly deals with structured, semi-structured, and unstructured dataDeals with structured and semi-structured dataSome applications of AI are virtual assistants such as Siri, chatbots, intelligent humanoid robot, etc.Applications of ML are recommendation system, search algorithms, Facebook auto friend tagging system, etc. Difference Between Data Science and Machine Learning. Data Science Machine LearningData Science helps with creating insights from data that deals with real world complexitiesMachine Learning helps in accurately predicting or classifying outcomes for new data points by learning patterns from historical dataPreferred skill-set:– domain expertise– strong SQL– ETL and data profiling– NoSQL systems, Standard reporting, VisualizationPreferred skill-set:– Python/ R Programming – Strong Mathematics Knowledge– Data Wrangling– SQL Model specific visualizationHorizontally scalable systems preferred to handle massive dataGPUs are preferred for intensive vector operationsComponents for handling unstructured raw dataMajor complexity is with the algorithms and mathematical concepts behind themMost of the input data is in human consumable formInput data is transformed specifically for the type of algorithms used Our Most Popular Free Courses: > < Beginner 1.0 Hours Introduction to Artificial Intelligence ★ 4.47 (200 Ratings) Free Enrol Now → Beginner 2.5 Hours Basics of Machine Learning ★ 4.34 (1506 Ratings) Free Enrol Now → Beginner 2.0 Hours Data Science Foundations ★ 4.43 (3282 Ratings) Free Enrol Now → Relationship between Data Science, Artificial Intelligence and Machine Learning . Artificial Intelligence and data science are a wide field of applications, systems and more that aim at replicating human intelligence through machines. Artificial Intelligence represents an action planned feedback of perception. Perception > Planning > Action > Feedback of PerceptionData Science uses different parts of this pattern or loop to solve specific problems. For instance, in the first step, i.e. Perception, data scientists try to identify patterns with the help of the data. Similarly, in the next step, i.e. planning, there are two aspects: Finding all possible solutionsFinding the best solution among all solutions Data science creates a system that interrelates both the aforementioned points and helps businesses move forward. Although it’s possible to explain machine learning by taking it as a standalone subject, it can best be understood in the context of its environment, i.e., the system it’s used within. Simply put, machine learning is the link that connects Data Science and AI. That is because it’s the process of learning from data over time. So, AI is the tool that helps data science get results and solutions for specific problems. However, machine learning is what helps in achieving that goal. A real-life example of this is Google’s Search Engine. Google’s search engine is a product of data scienceIt uses predictive analysis, a system used by artificial intelligence, to deliver intelligent results to the usersFor instance, if a person types “best jackets in NY” on Google’s search engine, then the AI collects this information through machine learningNow, as soon as the person writes these two words in the search tool “best place to buy,” the AI kicks in, and with predictive analysis completes the sentence as “best place to buy jackets in NY” which is the most probable suffix to the query that the user had in mind. A visual representation of the linkage between Data Science vs Machine Learning vs Artificial Intelligence. To be precise, Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology — Deep Learning. Deep Learning is a form of machine learning but differs in the use of Neural Networks where we stimulate the function of a brain to a certain extent and use a 3D hierarchy in data to identify patterns that are much more useful. Difference Between Data Science, Artificial Intelligence and Machine Learning. Although the terms Data Science vs Machine Learning vs Artificial Intelligence might be related and interconnected, each of them are unique in their own ways and are used for different purposes. Data Science is a broad term, and Machine Learning falls within it. Here’s the key difference between the terms.  Artificial Intelligence  Machine Learning Data Science Includes Machine Learning. Subset of Artificial Intelligence. Includes various Data Operations. Artificial Intelligence combines large amounts of data through iterative processing and intelligent algorithms to help computers learn automatically. Machine Learning uses efficient programs that can use data without being explicitly told to do so. Data Science works by sourcing, cleaning, and processing data to extract meaning out of it for analytical purposes.  Some of the popular tools that AI uses are-1. TensorFlow2. Scikit Learn3. Keras The popular tools that Machine Learning makes use of are-1. Amazon Lex2. IBM Watson Studio3. Microsoft Azure ML Studio Some of the popular tools used by Data Science are-1. SAS2. Tableau3. Apache Spark4. MATLAB Artificial Intelligence uses logic and decision trees.  Machine Learning uses statistical models.  Data Science deals with structured and unstructured data.  Chatbots, and Voice assistants are popular applications of AI.  Recommendation Systems such as Spotify, and Facial Recognition are popular examples. Fraud Detection and Healthcare analysis are popular examples of Data Science.  Explore all the free courses at Great Learning Academy, get certificates for free and learn in demand skills.  Start Now Read Also: Difference Between Data Science & Business Analytics Machine Learning vs Data Science Salary. A Machine Learning Engineer is an avid programmer who helps machines understand and pick up knowledge as required. The core role of a Machine Learning Engineer would be to create programs that enable a machine to take specific actions without any explicit programming. Their main responsibilities consist of data sets for analysis, personalising web experiences, and identifying business requirements. Salaries of a Machine Learning Engineer vs Data Scientist can vary based on skills, experience and companies hiring. Machine Learning Engineer Salary. Company Salary Deloitte  ₹ 6,51,000 PA Amazon ₹ 8,26,000 PA Accenture ₹15,40,000 PA Salary by Experience Experience Level Salary Beginner (1-2 years) ₹ 5,02,000 PA Mid-Senior (5-8 years) ₹ 6,81,000 PA Expert (10-15 years) ₹ 20,00,000 PA Data scientists are professionals who source, gather and analyse huge sets of data. Most of the business decisions today are based on insights drawn from analysing data, this is why a Data Scientist is crucial in today’s world. They work on modelling and processing structured and unstructured data, and also work on interpreting the findings into actionable plans for stakeholders. Data Scientist Salary. Company Salary Microsoft ₹ 1,500,000 PA Accenture ₹ 10,55,500 PA Tata Consultancies ₹ 5,94,050 PA Experience Level Salary  Beginner (1-2 years) ₹ 6,11,000 PA Mid-Senior (5-8 years) ₹ 10,00,000 PA Expert (10-15 years) ₹ 20,00,000 PA This is one of the major differences between Data Scientist vs Machine Learning Engineer. Data Science, Artificial Intelligence and Machine Learning Jobs. Data Science, Artificial Intelligence and Machine Learning are lucrative career options. However, truth is neither of the fields are mutually exclusive. There’s often an overlap when it comes to the skillset required for jobs in these domains. Data Science roles such as Data Analyst, Data Science Engineer, and Data Scientist are trending for quite some time. These jobs not only offer great salaries but also a lot of opportunity for growth. Some Requirements of Data Science associated Roles. Programming knowledgeData visualisation and reportingStatistical analysis and mathRisk analysisMachine learning techniquesData warehousing and structure Whether it is report-making or breaking down these reports to other stakeholders, a job in this domain is not limited to just programming or data mining. Every role in this field act as a bridging element between the technological and operational department, it is crucial for them to have excellent interpersonal skills apart from the technical know-how. Similarly, Artificial Intelligence and Machine Learning jobs are absorbing a huge chunk of talent off the market. Roles such as Machine Learning Engineer, Artificial Intelligence Architect, AI Research Specialist and similar jobs fall into this domain. Technical Skills required for AI-ML Roles. Knowledge of programming languages like Python, C++, JavaData modelling and evaluationProbability and statisticsDistributed computingMachine Learning algorithms As you can see, the skillset requirement of both domains overlap. In most cases, courses on data science and AI-ML include basic knowledge on both apart from the focus on the respective specializations. Even though the areas of data science vs machine learning vs artificial intelligence overlap, their specific functionalities differ and have respective areas of application. The data science market has opened up several services and product industries, creating opportunities for experts in this domain. Explore all the free courses at Great Learning Academy, get the certificates for free and learn in demand skills.  Faqs about Data Science vs Machine Learning and Artificial Intelligence. 1. Are Machine Learning and Data Science the same? Ans: No, Machine Learning and Data Science are not the same. They are two different domains of technology that work on two different aspects of businesses around the world. While Machine Learning focuses on enabling machines to self-learn and execute any task, Data science focuses on using data to help businesses analyse and understand trends. However, that’s not to say that there isn’t any overlap between the two domains. Both Machine Learning and Data Science depend on each other for various kinds of applications as data is indispensable and ML technologies are fast becoming an integral part of most industries.  2. Which is better, Machine Learning or Data Science? Ans: To begin with, one cannot compare the two domains to decide which is better – precisely because they are two different branches of studies. It is like comparing science and arts. However, one cannot deny the obvious popularity of data science today. Almost all the industries have taken recourse to data to arrive at more robust business decisions. Data has become an integral part of businesses, whether it is for analysing performance or device data-powered strategies or applications. Machine Learning, on the other hand, is still an evolving branch which is yet to be adopted by a few industries which only goes on to say that ML technologies will have more demand relevance in the near future. So, professionals of both these domains will be in equal demands in the future.  3. Is Data Science required for Machine Learning? Ans: Since both Machine Learning and Data Science are closely connected, a basic knowledge of each is required to specialise in either of the two domains. Having said that, more than data science the knowledge of data analysis is required to get started with Machine Learning. Learning programming languages like R, Python and Java are required to understand and clean data to use it for creating ML algorithms. Most Machine Learning courses include tutorials on these programming languages and basic data analysis and data science concepts.  4. Who earns more, Data Scientist or Machine Learning Engineer? Ans: Both Data Scientists and Machine Learning Engineers are quite in-demand roles in the market today. If you consider the entry-level jobs, then data scientists seem to earn more than Machine Learning engineers. An average data science salary for entry-level roles is more than 6 LPA, whereas, for Machine Learning engineers, it is around 5 LPA. However, when it comes to senior experts, professionals from both domains earn equally well, averaging around 20 LPA. 5. What is the Future of Data Science? Ans: Putting it slightly differently – Data Science is the future. No businesses or industries for that matter will be able to keep up without data science. A large number of transitions have already happened worldwide where businesses are seeking more data-driven decisions, more is to follow suit. Data science quite rightly has been dubbed as the oil of the 21st century which can mean endless possibilities across industries. So, if you are keen on pursuing this path, your efforts will be highly rewarded with not just a fulfilling career and fat pay cheques but also a lot of job security. 6. Can a Data Scientist become a Machine Learning Engineer? Ans: Yes, Data Scientists can become Machine Learning. In fact, it will not be very difficult for data scientists to transition to a Machine Learning career since they would have anyway worked closely on Data Science technologies that are frequently used in Machine Learning. Machine Learning languages, libraries and more are often used in data science applications as well. So data science professionals do not need to put in a humongous amount of effort to make this transition. So yes, with the right kind of upskilling course, data scientists can become machine learning engineers.  Further Reading. Machine Learning Tutorial For Complete Beginners | Learn Machine Learning with PythonData Science Tutorial For Beginners | Learn Data Science Complete TutorialArtificial Intelligence Tutorial for Beginners | Learn AI Tutorial from ExpertsDeep Learning Tutorial: What it Means and what’s the role of Deep LearningPython Tutorial For Beginners – A Complete Guide | Learn Python Easily “KickStart your Artificial Intelligence Journey with Great Learning which offers high-rated Artificial Intelligence courses with world-class training by industry leaders. Whether you’re interested in machine learning, data mining, or data analysis, Great Learning has a course for you!” 50 RELATED ARTICLESMORE FROM AUTHOR. Business Trends How Applying Data Science in E-Commerce Will Boost Online Sales. Data Science Top 7 Movies Every Data Scientist Must Watch. Career Top 6 Data Science Projects To Get You Hired in 2022. Great Learning is an ed-tech company that offers impactful and industry-relevant programs in high-growth areas. With a strong presence across the globe, we have empowered 10,000+ learners from over 50 countries in achieving positive outcomes for their careers. Know More FacebookInstagramLinkedinPinterestTwitter Data Science Courses Business Analytics Course PGP – Data Science & Engineering PGP – Business Analytics PGP – Data Science and Business Analytics PG Program in Data Science and Analytics M.Tech – Data Science and Machine Learning Business Analytics Certificate Program PGP – Artificial Intelligence & Machine Learning PGP – Machine Learning PGP – Artificial Intelligence for Leaders Post Graduate Program in Cloud Computing Stanford Cyber Security Course Stanford Design Thinking Course PGP – Strategic Digital Marketing All Software Engineering Courses Full Stack Developer Course What is Artificial Intelligence? What is Machine Learning? What is Data Science? 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                            [body] => High Energy Physics - Theory arXiv:2104.00008 (hep-th) [Submitted on 31 Mar 2021] Title:Why is AI hard and Physics simple? Authors:Daniel A. Roberts Download PDF Abstract: We discuss why AI is hard and why physics is simple. We discuss how physical intuition and the approach of theoretical physics can be brought to bear on the field of artificial intelligence and specifically machine learning. We suggest that the underlying project of machine learning and the underlying project of physics are strongly coupled through the principle of sparsity, and we call upon theoretical physicists to work on AI as physicists. As a first step in that direction, we discuss an upcoming book on the principles of deep learning theory that attempts to realize this approach. Comments: written for a special issue of Machine Learning: Science and Technology as an invited perspective piece Subjects: High Energy Physics - Theory (hep-th); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); History and Philosophy of Physics (physics.hist-ph); Machine Learning (stat.ML) Report number: MIT-CTP/5269 Cite as: arXiv:2104.00008 [hep-th]   (or arXiv:2104.00008v1 [hep-th] for this version) Submission history. From: Dan Roberts [view email] [v1] Wed, 31 Mar 2021 18:00:01 UTC (61 KB) Full-text links: Download:. PDF Other formats (license) Current browse context: hep-th < prev   |   next > new | recent | 2104 Change to browse by: cs cs.AI cs.LG physics physics.hist-ph stat stat.ML References & Citations. INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar a export bibtex citation Loading... Bibtex formatted citation. × loading... Data provided by: Bookmark. Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code & Data Code and Data Associated with this Article arXiv Links to Code Toggle arXiv Links to Code & Data (What is Links to Code & Data?) Related Papers Recommenders and Search Tools Connected Papers Toggle Connected Papers (What is Connected Papers?) Core recommender toggle CORE Recommender (What is CORE?) About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs and how to get involved. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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                            [body] => What is machine learning and how does it work?Machine learning is everywhere these days, but how does it work?By Calvin Wankhede•January 4, 2022 From personal assistants like Google Assistant and Alexa to content recommendations from YouTube and Amazon, it’s hard to think of a service or technology that machine learning hasn’t radically improved over the past few years.Simply put, machine learning is a subset of artificial intelligence that allows computers to learn from their own experiences — much like we do when learning or picking up a new skill. When implemented correctly, the technology can perform certain complex tasks better than any human, and often within seconds.Given how pervasive machine learning has become in today’s society, you may wonder how it works and what its limitations are. To that end, here’s a simple primer on the technology. Don’t worry if you don’t have a background in computer science — this article is just a high-level overview of what happens under the hood. What is machine learning?Even though many people conflate the terms machine learning (ML) and artificial intelligence (AI), there’s actually a distinction between the two. To understand why, it’s worth talking about how artificial intelligence started off in the first place.Early applications of AI, theorized around 50 years or so ago, were extremely basic by today’s standards. A chess game where you play against computer-controlled opponents, for instance, could once be considered revolutionary. It’s easy to see why — the ability to solve problems based on a set of rules can qualify as basic “intelligence”, after all. These days, however, we’d consider such a system extremely rudimentary as it lacks experience — a key component of human intelligence. This is where machine learning comes in. Machine learning enables computers to learn or train themselves from massive amounts of existing data. Machine learning adds an entirely new dimension to artificial intelligence — it enables computers to learn or train themselves from massive amounts of existing data. In this context, “learning” means forming relationships and extracting new patterns from a given set of data. This is a lot like how human intelligence works as well. When we come across something unfamiliar, we use our senses to study its features and can use our memory to recognize it the next time. How does machine learning work?GoogleBroadly speaking, a machine learning problem can be solved in two distinct phases: training and inference. In the first stage, a computer algorithm analyzes a bunch of sample or training data to extract relevant features and patterns. Each algorithm is generally optimized for a certain type of data. The data can be anything — numbers, images, text, and even speech.The success of the training process, meanwhile, is directly linked to three factors: the algorithm itself, the amount of data you feed it, and the dataset’s quality. Every now and then, researchers propose new algorithms or techniques that improve accuracy and reduce errors, as you’d expect from cutting-edge technology. Increasing the amount of data you offer the algorithm, on the other hand, can also help cover more edge cases. Machine learning programs involve two distinct stages: training and inference. The output of a machine learning algorithm is often referred to as a model. You can equate ML models to a dictionary or reference manual as it’s used for future predictions. In other words, we use trained models to infer results from new data that our program has never seen before.The training process usually involves analyzing thousands or even millions of samples. As you’d expect, this is a fairly hardware-intensive process that needs to be completed ahead of time. Once the training process is complete and all of the relevant features have been analyzed, however, some resulting models can be small enough to fit on common devices like smartphones.Consider a machine learning application that interprets handwritten text, for example. As part of the training process, a developer first feeds an ML algorithm with sample images. This eventually gives them an ML model that can be packaged and deployed within something like an Android application. When users install the app and feed it with new images of their own, their devices can reference the model to infer new results. In the real world, you won’t see any of this, of course — the app will simply convert handwritten words into digital text. Training a machine learning model is a hardware-intensive task that may take several hours or even days. While early machine learning applications relied on the cloud for training and inference, recent technological advancements have enabled local, on-device inference as well. Of course, this largely depends on the algorithm and hardware used — as we’ll discuss in a later section.For now, here’s a rundown of the various machine learning training techniques and how they differ from each other. Supervised, unsupervised, and reinforcement learning. In a nutshell, the data used to train the algorithm can fall under one of two categories: labeled and unlabelled. As you may have guessed from the title, supervised learning involves a labeled dataset, which helps the training algorithm know what it’s looking for.Take a model thats sole purpose is to identify images of dogs and cats, for example. If you feed the algorithm with labeled images of the two animals, it is simply a case of supervised learning. However, if you expect the algorithm to figure out the differentiating features all on its own (that is, without labels indicating the image contains a dog or cat), it becomes unsupervised learning.Unsupervised learning is especially useful in instances where you might not know what patterns to look for. Furthermore, new data is constantly fed back into the system for training — without any manual input required from a human.Say an ecommerce website like Amazon wants to create a targeted marketing campaign. They typically already know a lot about their customers, including their age, purchasing history, browsing habits, location, and much more. An unsupervised learning algorithm would be able to form relationships between these variables all by itself. It might help marketers realize that customers from a particular area tend to purchase certain types of clothing or that young shoppers are more likely to spend on recreational items. Whatever the case may be, it’s a completely hands-off number-crunching, discovery process. Unsupervised learning excels at finding patterns and relationships in a dataset that a human might otherwise overlook. All in all, unsupervised learning is a useful technique in scenarios that are not quite as straightforward as those with known outcomes.Finally, we have reinforcement learning, which works particularly well in applications that have many ways to reach a clear goal. It’s a system of trial and error — positive actions are rewarded, while negative ones are discarded. This means the model can evolve based on its own experiences over time.A game of chess is the perfect application for reinforcement learning because the algorithm can learn from its mistakes. In fact, Google’s DeepMind subsidiary built an ML program that used reinforcement learning to become better at the board game, Go. Between 2016 and 2017, it went on to defeat multiple Go world champions in competitive settings — a remarkable achievement, to say the least. What about neural networks and what is deep learning?A neural network is a specific subtype of machine learning inspired by the behavior of the human brain. Biological neurons in an animal body are responsible for sensory processing. They take information from our surroundings and transmit electrical signals over long distances to the brain. Our bodies have billions of such neurons that all communicate with each other, helping us see, feel, hear, and everything in between. An artificial neural network mimics the behavior of biological neurons in an animal body. In that vein, artificial neurons in a neural network talk to each other as well. They break down complex problems into smaller chunks or “layers”. Each layer is made up of neurons (also called nodes) that accomplish a specific task and communicate their results with nodes in the next layer. In a neural network trained to recognize objects, for example, you’ll have one layer with neurons that detect edges, another that looks at changes in color, and so on.Layers are linked to each other, so “activating” a particular chain of neurons gives you a certain predictable output. Because of this multi-layer approach, neural networks excel at solving complex problems. Consider autonomous or self-driving vehicles, for instance. They use a myriad of sensors and cameras to detect roads, signage, pedestrians, and obstacles. All of these variables have some complex relationship with each other, making it a perfect application for a multi-layered neural network.Deep learning is a term that’s often used to describe a neural network with many layers. The term “deep” here simply refers to the layer depth. Where do we see machine learning in our daily lives?Robert Triggs / Android AuthorityMachine learning influences pretty much every aspect of our digital lives. Social media platforms like Instagram, for example, often show you targeted advertisements based on the posts you interact with. If you like an image containing food, you might get advertisements related to meal kits or nearby restaurants. Similarly, streaming services like YouTube and Netflix can infer new genres and topics you may be interested in, based on your watch history and duration.Even on personal devices like smartphones, features such as facial recognition rely heavily on machine learning. Take the Google Photos app, for example. It not only detects faces from your photos but also uses machine learning to identify unique facial features for each individual. The pictures you upload help improve the system, allowing it to make more accurate predictions in the future. The app also often prompts you to verify if a certain match is accurate — indicating that the system has a low confidence level in that particular prediction. See also: How on-device machine learning has changed the way we use our phonesIndeed, machine learning is all about achieving reasonably high accuracy in the least amount of time. It’s not always successful, of course.In 2016, Microsoft unveiled a state-of-the-art chatbot named Tay. As a showcase of its human-like conversational abilities, the company allowed Tay to interact with the public through a Twitter account. However, the project was taken offline within just 24 hours after the bot began responding with derogatory remarks and other inappropriate dialogue.The above example highlights an important point — machine learning is only really useful if the training data is reasonably high quality and aligns with your end goal. Tay was trained on live Twitter submissions, meaning it was easily manipulated or trained by malicious actors. Machine learning isn't a one-size-fits-all arrangement. It requires careful planning, a varied and clean data set, and occasional supervision. Dangers of machine learning aside, the technology can also help in scenarios where traditional methods just cannot keep pace.Rendering graphically complex video games represents one such application. For decades, we’ve relied on yearly performance increases to achieve this task. However, processing power has started to plateau of late — even as other technologies like display resolutions and refresh rates continue to march upwards.ML-based upscaling technologies like Nvidia’s Deep Learning Supersampling (DLSS) are helping bridge this gap. The way DLSS works is rather straightforward — the GPU first renders an image at a lower resolution and then uses a trained ML model to upscale it. The results are impressive, to say the least — far better than traditional, non-ML upscaling technologies. Similarly, super-resolution upscaling is used to improve smartphone photography image quality. Machine learning isn’t just for basic predictions anymore. How does hardware affect machine learning performance?Edgar Cervantes / Android AuthorityMany of the aforementioned machine learning applications, including facial recognition and ML-based image upscaling, were once impossible to accomplish on consumer-grade hardware. In other words, you had to connect to a powerful server sitting in a data center to accomplish most ML-related tasks.Even today, training an ML model is extremely hardware intensive and pretty much requires dedicated hardware for larger projects. Since training involves running a small number of algorithms repeatedly, though, manufacturers often design custom chips to achieve better performance and efficiency. These are called application-specific integrated circuits or ASICs. Large-scale ML projects typically make use of either ASICs or GPUs for training, and not general-purpose CPUs. These offer higher performance and lower power consumption than a traditional CPU. Machine learning accelerators help improve inference efficiency, making it possible to deploy ML apps to more and more devices. Things have started to change, however, at least on the inference side of things. On-device machine learning is starting to become more commonplace on devices like smartphones and laptops. This is thanks to the inclusion of dedicated, hardware-level ML accelerators within modern processors and SoCs. Read more: Why are smartphone chips suddenly including an AI processor?Machine learning accelerators are extremely power efficient compared to an ordinary processor. This is why the DLSS upscaling technology we spoke about earlier, for example, is only available on newer Nvidia graphics cards with the requisite ML acceleration hardware. In smartphones, we’ve seen specific low-power accelerators designed for voice detection and a growing trend in ML processing power integrated tightly with more traditional image processors for better photography.Going forward, we’re likely to see feature segmentation and exclusivity depending on each new hardware generation’s machine learning acceleration capabilities. In fact, we’re already witnessing that happen in the smartphone industry. Machine learning at the edge: Smartphones and consumer devices. Ryan Haines / Android AuthorityML accelerators have been built into smartphone SoCs for a while now. However, they’ve become a key focal point of late due to the rise of use-cases like computational photography and voice recognition.In 2021, Google announced its first semi-custom SoC, nicknamed Tensor, for the Pixel 6. One of Tensor’s key differentiators was its custom TPU — or Tensor Processing Unit. Google claims that its chip delivers significantly faster ML inference versus the competition, especially in areas such as natural language processing. This, in turn, allowed Google to use Tensor for a suite of new features on the Pixel 6, including real-time language translation, HDR-enabled video recording, and faster speech-to-text functionality. Smartphone processors from MediaTek, Qualcomm, and Samsung have their own takes on dedicated ML hardware too. See also: What is Google Tensor?That’s not to say that cloud-based inference isn’t still in use today — quite the opposite, in fact. While on-device machine learning has become increasingly common, it’s still far from ideal. This is especially true when we consider complex problems like voice recognition and image classification. Voice assistants like Amazon’s Alexa and Google Assistant are only as good as they are today because they rely on powerful cloud infrastructure — for both inference as well as model re-training. On-device machine learning enabled a plethora of futuristic smartphone features, including computational photography, real-time translation, and live captions. However, as with most new technologies, new solutions and techniques are constantly on the horizon. In 2017, Google’s HDRnet algorithm revolutionized smartphone imaging, while MobileNet brought down the size of ML models and made on-device inference feasible. More recently, the company highlighted how it uses a privacy-preserving technique called federated learning to train machine learning models with user-generated data.Apple, meanwhile, also integrates hardware ML accelerators within all of its consumer chips these days. The Apple M1 family of SoCs included in the latest Macbooks, for instance, has enough machine learning grunt to perform training tasks on the device itself. And with that, you’re now up to speed on the basics of machine learning! If you’re looking to get started with the technology on your own, consider checking out our guide on adding machine learning to an Android app. The best phones under £200 in the UKThe BestFeaturesComments
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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] => An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with ExamplesThis Machine Learning tutorial introduces the basics of ML theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic.AuthorAuthorNick McCreaNicholas is a professional software engineer with a passion for quality craftsmanship. He loves architecting and writing top-notch code.SHARESHARERead the Spanish version of this article translated by Marisela OrdazMachine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This Machine Learning tutorial introduces the basics of ML theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with machine learning basics. What is Machine Learning? So what exactly is “machine learning” anyway? ML is actually a lot of things. The field is quite vast and is expanding rapidly, being continually partitioned and sub-partitioned ad nauseam into different sub-specialties and types of machine learning. There are some basic common threads, however, and the overarching theme is best summed up by this oft-quoted statement made by Arthur Samuel way back in 1959: “[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.” And more recently, in 1997, Tom Mitchell gave a “well-posed” definition that has proven more useful to engineering types: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” -- Tom Mitchell, Carnegie Mellon University So if you want your program to predict, for example, traffic patterns at a busy intersection (task T), you can run it through a machine learning algorithm with data about past traffic patterns (experience E) and, if it has successfully “learned”, it will then do better at predicting future traffic patterns (performance measure P). The highly complex nature of many real-world problems, though, often means that inventing specialized algorithms that will solve them perfectly every time is impractical, if not impossible. Examples of machine learning problems include, “Is this cancer?”, “What is the market value of this house?”, “Which of these people are good friends with each other?”, “Will this rocket engine explode on take off?”, “Will this person like this movie?”, “Who is this?”, “What did you say?”, and “How do you fly this thing?”. All of these problems are excellent targets for an ML project, and in fact ML has been applied to each of them with great success. ML solves problems that cannot be solved by numerical means alone. Among the different types of ML tasks, a crucial distinction is drawn between supervised and unsupervised learning: Supervised machine learning: The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data. Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein. We will primarily focus on supervised learning here, but the end of the article includes a brief discussion of unsupervised learning with some links for those who are interested in pursuing the topic further. Supervised Machine Learning. In the majority of supervised learning applications, the ultimate goal is to develop a finely tuned predictor function h(x) (sometimes called the “hypothesis”). “Learning” consists of using sophisticated mathematical algorithms to optimize this function so that, given input data x about a certain domain (say, square footage of a house), it will accurately predict some interesting value h(x) (say, market price for said house). In practice, x almost always represents multiple data points. So, for example, a housing price predictor might take not only square-footage (x1) but also number of bedrooms (x2), number of bathrooms (x3), number of floors (x4), year built (x5), zip code (x6), and so forth. Determining which inputs to use is an important part of ML design. However, for the sake of explanation, it is easiest to assume a single input value is used. So let’s say our simple predictor has this form: where and are constants. Our goal is to find the perfect values of and to make our predictor work as well as possible. Optimizing the predictor h(x) is done using training examples. For each training example, we have an input value x_train, for which a corresponding output, y, is known in advance. For each example, we find the difference between the known, correct value y, and our predicted value h(x_train). With enough training examples, these differences give us a useful way to measure the “wrongness” of h(x). We can then tweak h(x) by tweaking the values of and to make it “less wrong”. This process is repeated over and over until the system has converged on the best values for and . In this way, the predictor becomes trained, and is ready to do some real-world predicting. Machine Learning Examples. We stick to simple problems in this post for the sake of illustration, but the reason ML exists is because, in the real world, the problems are much more complex. On this flat screen we can draw you a picture of, at most, a three-dimensional data set, but ML problems commonly deal with data with millions of dimensions, and very complex predictor functions. ML solves problems that cannot be solved by numerical means alone. With that in mind, let’s look at a simple example. Say we have the following training data, wherein company employees have rated their satisfaction on a scale of 1 to 100: First, notice that the data is a little noisy. That is, while we can see that there is a pattern to it (i.e. employee satisfaction tends to go up as salary goes up), it does not all fit neatly on a straight line. This will always be the case with real-world data (and we absolutely want to train our machine using real-world data!). So then how can we train a machine to perfectly predict an employee’s level of satisfaction? The answer, of course, is that we can’t. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. It is somewhat reminiscent of the famous statement by British mathematician and professor of statistics George E. P. Box that “all models are wrong, but some are useful”. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Machine Learning builds heavily on statistics. For example, when we train our machine to learn, we have to give it a statistically significant random sample as training data. If the training set is not random, we run the risk of the machine learning patterns that aren’t actually there. And if the training set is too small (see law of large numbers), we won’t learn enough and may even reach inaccurate conclusions. For example, attempting to predict company-wide satisfaction patterns based on data from upper management alone would likely be error-prone. With this understanding, let’s give our machine the data we’ve been given above and have it learn it. First we have to initialize our predictor h(x) with some reasonable values of and . Now our predictor looks like this when placed over our training set: If we ask this predictor for the satisfaction of an employee making $60k, it would predict a rating of 27: It’s obvious that this was a terrible guess and that this machine doesn’t know very much. So now, let’s give this predictor all the salaries from our training set, and take the differences between the resulting predicted satisfaction ratings and the actual satisfaction ratings of the corresponding employees. If we perform a little mathematical wizardry (which I will describe shortly), we can calculate, with very high certainty, that values of 13.12 for and 0.61 for are going to give us a better predictor. And if we repeat this process, say 1500 times, our predictor will end up looking like this: At this point, if we repeat the process, we will find that and won’t change by any appreciable amount anymore and thus we see that the system has converged. If we haven’t made any mistakes, this means we’ve found the optimal predictor. Accordingly, if we now ask the machine again for the satisfaction rating of the employee who makes $60k, it will predict a rating of roughly 60. Now we’re getting somewhere. Machine Learning Regression: A Note on Complexity. The above example is technically a simple problem of univariate linear regression, which in reality can be solved by deriving a simple normal equation and skipping this “tuning” process altogether. However, consider a predictor that looks like this: This function takes input in four dimensions and has a variety of polynomial terms. Deriving a normal equation for this function is a significant challenge. Many modern machine learning problems take thousands or even millions of dimensions of data to build predictions using hundreds of coefficients. Predicting how an organism’s genome will be expressed, or what the climate will be like in fifty years, are examples of such complex problems. Many modern ML problems take thousands or even millions of dimensions of data to build predictions using hundreds of coefficients. Fortunately, the iterative approach taken by ML systems is much more resilient in the face of such complexity. Instead of using brute force, a machine learning system “feels its way” to the answer. For big problems, this works much better. While this doesn’t mean that ML can solve all arbitrarily complex problems (it can’t), it does make for an incredibly flexible and powerful tool. Gradient Descent - Minimizing “Wrongness”. Let’s take a closer look at how this iterative process works. In the above example, how do we make sure and are getting better with each step, and not worse? The answer lies in our “measurement of wrongness” alluded to previously, along with a little calculus. The wrongness measure is known as the cost function (a.k.a., loss function), . The input represents all of the coefficients we are using in our predictor. So in our case, is really the pair and . gives us a mathematical measurement of how wrong our predictor is when it uses the given values of and . The choice of the cost function is another important piece of an ML program. In different contexts, being “wrong” can mean very different things. In our employee satisfaction example, the well-established standard is the linear least squares function: With least squares, the penalty for a bad guess goes up quadratically with the difference between the guess and the correct answer, so it acts as a very “strict” measurement of wrongness. The cost function computes an average penalty over all of the training examples. So now we see that our goal is to find and for our predictor h(x) such that our cost function is as small as possible. We call on the power of calculus to accomplish this. Consider the following plot of a cost function for some particular Machine Learning problem: Here we can see the cost associated with different values of and . We can see the graph has a slight bowl to its shape. The bottom of the bowl represents the lowest cost our predictor can give us based on the given training data. The goal is to “roll down the hill”, and find and corresponding to this point. This is where calculus comes in to this machine learning tutorial. For the sake of keeping this explanation manageable, I won’t write out the equations here, but essentially what we do is take the gradient of , which is the pair of derivatives of (one over and one over ). The gradient will be different for every different value of and , and tells us what the “slope of the hill is” and, in particular, “which way is down”, for these particular s. For example, when we plug our current values of into the gradient, it may tell us that adding a little to and subtracting a little from will take us in the direction of the cost function-valley floor. Therefore, we add a little to , and subtract a little from , and voilà! We have completed one round of our learning algorithm. Our updated predictor, h(x) = + x, will return better predictions than before. Our machine is now a little bit smarter. This process of alternating between calculating the current gradient, and updating the s from the results, is known as gradient descent. That covers the basic theory underlying the majority of supervised Machine Learning systems. But the basic concepts can be applied in a variety of different ways, depending on the problem at hand. Classification Problems in Machine Learning. Under supervised ML, two major subcategories are: Regression machine learning systems: Systems where the value being predicted falls somewhere on a continuous spectrum. These systems help us with questions of “How much?” or “How many?”. Classification machine learning systems: Systems where we seek a yes-or-no prediction, such as “Is this tumer cancerous?”, “Does this cookie meet our quality standards?”, and so on. As it turns out, the underlying Machine Learning theory is more or less the same. The major differences are the design of the predictor h(x) and the design of the cost function . Our examples so far have focused on regression problems, so let’s now also take a look at a classification example. Here are the results of a cookie quality testing study, where the training examples have all been labeled as either “good cookie” (y = 1) in blue or “bad cookie” (y = 0) in red. In classification, a regression predictor is not very useful. What we usually want is a predictor that makes a guess somewhere between 0 and 1. In a cookie quality classifier, a prediction of 1 would represent a very confident guess that the cookie is perfect and utterly mouthwatering. A prediction of 0 represents high confidence that the cookie is an embarrassment to the cookie industry. Values falling within this range represent less confidence, so we might design our system such that prediction of 0.6 means “Man, that’s a tough call, but I’m gonna go with yes, you can sell that cookie,” while a value exactly in the middle, at 0.5, might represent complete uncertainty. This isn’t always how confidence is distributed in a classifier but it’s a very common design and works for purposes of our illustration. It turns out there’s a nice function that captures this behavior well. It’s called the sigmoid function, g(z), and it looks something like this: z is some representation of our inputs and coefficients, such as: so that our predictor becomes: Notice that the sigmoid function transforms our output into the range between 0 and 1. The logic behind the design of the cost function is also different in classification. Again we ask “what does it mean for a guess to be wrong?” and this time a very good rule of thumb is that if the correct guess was 0 and we guessed 1, then we were completely and utterly wrong, and vice-versa. Since you can’t be more wrong than absolutely wrong, the penalty in this case is enormous. Alternatively if the correct guess was 0 and we guessed 0, our cost function should not add any cost for each time this happens. If the guess was right, but we weren’t completely confident (e.g. y = 1, but h(x) = 0.8), this should come with a small cost, and if our guess was wrong but we weren’t completely confident (e.g. y = 1 but h(x) = 0.3), this should come with some significant cost, but not as much as if we were completely wrong. This behavior is captured by the log function, such that: Again, the cost function gives us the average cost over all of our training examples. So here we’ve described how the predictor h(x) and the cost function differ between regression and classification, but gradient descent still works fine. A classification predictor can be visualized by drawing the boundary line; i.e., the barrier where the prediction changes from a “yes” (a prediction greater than 0.5) to a “no” (a prediction less than 0.5). With a well-designed system, our cookie data can generate a classification boundary that looks like this: Now that’s a machine that knows a thing or two about cookies! An Introduction to Neural Networks. No discussion of Machine Learning would be complete without at least mentioning neural networks. Not only do neural nets offer an extremely powerful tool to solve very tough problems, but they also offer fascinating hints at the workings of our own brains, and intriguing possibilities for one day creating truly intelligent machines. Neural networks are well suited to machine learning models where the number of inputs is gigantic. The computational cost of handling such a problem is just too overwhelming for the types of systems we’ve discussed above. As it turns out, however, neural networks can be effectively tuned using techniques that are strikingly similar to gradient descent in principle. A thorough discussion of neural networks is beyond the scope of this tutorial, but I recommend checking out our previous post on the subject. Unsupervised Machine Learning. Unsupervised machine learning is typically tasked with finding relationships within data. There are no training examples used in this process. Instead, the system is given a set data and tasked with finding patterns and correlations therein. A good example is identifying close-knit groups of friends in social network data. The Machine Learning algorithms used to do this are very different from those used for supervised learning, and the topic merits its own post. However, for something to chew on in the meantime, take a look at clustering algorithms such as k-means, and also look into dimensionality reduction systems such as principle component analysis. Our prior post on big data discusses a number of these topics in more detail as well. Conclusion. We’ve covered much of the basic theory underlying the field of Machine Learning here, but of course, we have only barely scratched the surface. Keep in mind that to really apply the theories contained in this introduction to real life machine learning examples, a much deeper understanding of the topics discussed herein is necessary. There are many subtleties and pitfalls in ML, and many ways to be lead astray by what appears to be a perfectly well-tuned thinking machine. Almost every part of the basic theory can be played with and altered endlessly, and the results are often fascinating. Many grow into whole new fields of study that are better suited to particular problems. Clearly, Machine Learning is an incredibly powerful tool. In the coming years, it promises to help solve some of our most pressing problems, as well as open up whole new worlds of opportunity for data science firms. The demand for Machine Learning engineers is only going to continue to grow, offering incredible chances to be a part of something big. I hope you will consider getting in on the action! Acknowledgement. This article draws heavily on material taught by Stanford Professor Dr. Andrew Ng in his free and open Machine Learning course. The course covers everything discussed in this article in great depth, and gives tons of practical advice for the ML practitioner. I cannot recommend this course highly enough for those interested in further exploring this fascinating field. Related: Sound Logic and Monotonic AI Models Schooling Flappy Bird: A Reinforcement Learning Tutorial Understanding the basics. What is Deep Learning?Deep learning is a machine learning method that relies on artificial neural networks, allowing computer systems to learn by example. In most cases, deep learning algorithms are based on information patterns found in biological nervous systems.What is Machine Learning?As described by Arthur Samuel, Machine Learning is the "field of study that gives computers the ability to learn without being explicitly programmed."Machine Learning vs Artificial Intelligence: What’s the difference?Artificial Intelligence (AI) is a broad term used to describe systems capable of making certain decisions on their own. Machine Learning (ML) is a specific subject within the broader AI arena, describing the ability for a machine to improve its ability by practicing a task or being exposed to large data sets.How to learn Machine Learning?Machine Learning requires a great deal of dedication and practice to learn, due to the many subtle complexities involved in ensuring your machine learns the right thing and not the wrong thing. An excellent online course for Machine Learning is Andrew Ng's Coursera course.What is overfitting in Machine Learning?Overfitting is the result of focussing a Machine Learning algorithm too closely on the training data, so that it is not generalized enough to correctly process new data. It is an example of a machine "learning the wrong thing" and becoming less capable of correctly interpreting new data.What is a Machine Learning model?A Machine Learning model is a set of assumptions about the underlying nature the data to be trained for. The model is used as the basis for determining what a Machine Learning algorithm should learn. A good model, which makes accurate assumptions about the data, is necessary for the machine to give good resultsWorld-class articles, delivered weekly.Subscription implies consent to our privacy policyThank you!Check out your inbox to confirm your invite.World-class articles, delivered weekly.Subscription implies consent to our privacy policyThank you!Check out your inbox to confirm your invite.Toptal Developers. Join the Toptal® community. Hire a DeveloperORApply as a DeveloperBy continuing to use this site you agree to our Cookie Policy.Got it
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                            [body] => Improving a Machine Learning System (Part 1 - Broken Abstractions) Posted on November 6, 2021 This post is part one in a three part series on the challenges of improving a production machine learning system. Find part two here and part three here. Suppose you have been hired to apply state of the art machine learning technology to improve the Foo vs Bar classifier at FooBar International. Foo vs Bar classification is a critical business need for FooBar International, and the company has been using a simple system based on a decade-old machine learning technology to solve this problem for the last several years. As a machine learning expert, you are shocked that FooBar International hasn’t gotten around to modernizing this system, and you are confident that replacing the Foo vs Bar classifier with the latest machine learning hotness will dramatically improve system performance. You pull the Foo vs Bar training data into a notebook, spend a few weeks experimenting with features and model architectures, and soon see a small increase in performance on the holdout set. Next, you work with the engineering team to run an A/B test comparing your new model to the existing system. To your surprise, your new model substantially underperforms compared to the existing system. This is a familiar story that anybody who has built machine learning models at a large company will recognize. Making measurable improvements to a mature machine learning system is extremely difficult. In this post, we will explore why. Broken Abstractions and Unstable Systems. Machine learning systems are extremely complex, and have a frustrating ability to erode abstractions between software components. This presents a wide array of challenges to the kind of iterative development that is essential for ML success. Justifying Infrastructure Investments. Most software systems carefully control which layers need to communicate with each other and which data needs to be exposed along each layer boundary. It is quite common for a new machine learning system to require breaking existing abstractions and connecting layers that were designed as separate. For example: A new feature normalization strategy may require exposing raw data to a part of the system that was designed to consume processed data. Migrating a feedforward neural network to a graph neural network may require accessing the features of a node’s neighbors at inference time. Adapting a model to consume the predictions of another model as features may require configuring the models to run in sequence rather than in parallel. Modifying software architecture and abstractions to support new products is common and healthy. However, it is difficult to execute these kinds of changes effectively before we have a clear understanding of what the next stable state of our software architecture should be. Unfortunately, this is exactly the case with machine learning improvements. Until we can test the machine learning system in production, we cannot determine how much value it will bring. This kind of catch-22 can dramatically slow down development and experimentation agility. In larger organizations where such a refactor may require multiple teams to prioritize the changes, this can lead to a state of paralysis. In some situations it may be possible to build a modified version of the new machine learning system that operates within the constraints of the current software abstractions. For example, perhaps the fancy new model can execute in a batch job rather than in realtime. However, it is usually difficult to estimate how much this kind of modified design will handicap model performance. It is quite common for a state-of-the-art machine learning system that is deployed under such a handicap to underperform a simpler system that has been optimized for the abstractions of the software system it lives within. Distribution Mismatch. It is very rare that the distribution of samples for which we have labels is the same as the distribution of samples for which we have to perform inference. For example: Click-through-rate (CTR) prediction models do not receive labels on samples that the user did not see. Most content moderation systems receive feedback on only a tiny, typically non-representative subset of data. Most sensor analytics algorithms are built on top of labeled datasets that have unrealistically low noise levels. In any of these scenarios, our model’s performance on labeled data presents only a skewed view of how the model performs on all traffic. There are some techniques like importance sampling that can minimize the impact of this bias, but these strategies can be quite hard to tune. To make matters worse, it is quite common for data distributions to shift over time. In some circumstances these shifts can happen quite rapidly: it is not uncommon for models at large social networking companies to go stale within hours (example). The combination of these effects can make it extremely difficult to draw conclusions about model performance improvements from offline results. In practice online A/B testing is usually required to draw more reliable conclusions by tracking metrics like revenue and report rate over all traffic. However, this substantially increases the iteration time required to make model improvements, which slows down progress. Feedback Loops. In many circumstances the labeled data that our model receives is dependent on the predictions it generates. Some classic examples of this situation include: Most recommender systems don’t receive user feedback on any sample that the user did not see. Systems that effectively detect and penalize users for certain behaviors may cause users to modify their behavior to avoid penalty. If a content moderation system is trained on user reports, then increases in system coverage may elicit decreases in model training data as user report rates decrease. Furthermore, labeled data is not the only kind of feedback loop that can plague machine learning systems. It is also quite common for the features that a machine learning system consumes to depend on its predictions. For example, a common feature in recommendation systems is the number of previous times that a user has seen content of type X. This count will be heavily influenced by how biased the recommendation system is towards recommending content of type X. This is a particularly insidious kind of distribution mismatch, since it can give rise to extremely complicated system dynamics. We can sometimes minimize the impact of feedback loops by deriving training data from specialized pipelines that do not use the machine learning model directly. However, this strategy can be difficult to scale, can sometimes hurt model performance by preventing the model from learning from its mistakes, and does not directly address the influence of feedback loops on model features. Unknown Tight Couplings. In production machine learning systems seemingly independent components often exhibit hidden tight couplings. This can make experimentation challenging. Changing one system without changing the other will cause performance to degrade, and changing both systems at the same time is often error prone and coordination intensive. Some examples include: Once a feature pipeline is developed and made available for model utilization, any change to that pipeline (even correcting errors!) risks damaging the performance of models that consume that feature. This forces ML engineers to version all feature changes, which causes feature pipelines to grow into unwieldy monsters extremely quickly. In large scale recommendation systems it is common for a light ML model or heuristic-based candidate generation system to select the set of candidates that a heavy machine learning model chooses between. Any change to the candidate generation system would affect the distribution of samples that are fed to the heavy model, which could affect that model’s performance. It is quite common for some models, such as semantic models or object detection models, to produce signals that other models consume as features. In this situation any change or improvement to the upstream models could damage the performance of the downstream models that consume their predictions. When designing or working with an ML system, it is extremely important to be aware of the tight couplings that exist. In certain situations it can be beneficial to introduce redundant components in a system in order to relax the number of components that are tightly coupled. To Be Continued. That’s all for now. In our next post we will explore some of the challenges of adding new features or improving existing features in a machine learning system. Tags: Machine Learning, Machine Learning Systems, Abstractions ← Previous Post Next Post →
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                            [body] => 9 Real-World Problems that can be Solved by Machine Learning Machine Learning has gained a lot of prominence in the recent years because of its ability to be applied across scores of industries to solve complex problems effectively and quickly. Contrary to what one might expect, Machine Learning use cases are not that difficult to come across. The most common examples of problems solved by machine learning are image tagging by Facebook and spam detection by email providers. Contents hide 1 What is Machine Learning? 2 Types Of Machine Learning 2.1 1. Supervised Learning 2.2 2. Unsupervised Learning 2.3 3. Reinforcement Learning 3 9 Real-World Problems Solved by Machine Learning 3.1 1. Identifying Spam 3.2 2. Making Product Recommendations 3.3 3. Customer Segmentation 3.4 4. Image & Video Recognition 3.5 5. Fraudulent Transactions 3.6 6. Demand Forecasting 3.7 7. Virtual Personal Assistant 3.8 8. Sentiment Analysis 3.9 9. Customer Service Automation 4 Wrapping Up Hey there! This blog is almost about 2000+ words long and may take ~8 mins to go through the whole thing. We understand that you might not have that much time. This is precisely why we made a short video on the topic. It is less than 2 mins, and summarizes how can Machine Learning be used in everyday life?. We hope this helps you learn more and save your time. Cheers! Machine Learning can resolve an incredible number of challenges across industry domains by working with the right datasets. In this post, we will learn about some typical problems solved by machine learning and how they enable businesses to leverage their data accurately. What is Machine Learning? . A sub-area of artificial intelligence – machine learning is IT systems’ ability to recognize patterns in large databases to independently find solutions to problems. Put simply; it is an umbrella term for various techniques and tools that can help computers learn and adapt on their own. Unlike traditional programming, which is a manually created program that uses input data and runs on a computer to produce the output, in Machine Learning or augmented analytics, the input data and output are given to an algorithm to create a program. It leads to powerful insights that can be used to predict future outcomes. Machine learning algorithms do all of that and more, using statistics to find patterns in vast amounts of data that encompasses everything from images, numbers, words, etc. If the data can be stored digitally, it can be fed into a machine-learning algorithm to solve specific problems. Types Of Machine Learning . Today, Machine Learning algorithms are primarily trained using three essential methods. These are categorized as three types of machine learning, as discussed below –     1. Supervised Learning . One of the most elementary types of machine learning, supervised learning, is one where data is labeled to inform the machine about the exact patterns it should look for. Although the data needs to be labeled accurately for this method to work, supervised learning is compelling and provides excellent results when used in the right circumstances. For instance, when we press play on a Netflix show, we’re informing the Machine Learning algorithm to find similar shows based on our preference. How it works – The Machine Learning algorithm here is provided with a small training dataset to work with, which is a smaller part of the bigger dataset. It serves to give the algorithm an idea of the problem, solution, and various data points to be dealt with. The training dataset here is also very similar to the final dataset in its characteristics and offers the algorithm with the labeled parameters required for the problem. The Machine Learning algorithm then finds relationships between the given parameters, establishing a cause and effect relationship between the variables in the dataset.     2. Unsupervised Learning . Unsupervised learning, as the name suggests, has no data labels. The machine looks for patterns randomly. It means that there is no human labor required to make the dataset machine-readable. It allows much larger datasets to be worked on by the program. Compared to supervised learning, unsupervised Machine Learning services aren’t much popular because of lesser applications in day-to-day life.  How does it work? Since unsupervised learning does not have any labels to work off, it creates hidden structures. Relationships between data points are then perceived by the algorithm randomly or abstractly, with absolutely no input required from human beings. Instead of a specific, defined, and set problem statement, unsupervised learning algorithms can adapt to the data by changing hidden structures dynamically.     3. Reinforcement Learning . Reinforcement learning primarily describes a class of machine learning problems where an agent operates in an environment with no fixed training dataset. The agent must know how to work using feedback. How does it work? Reinforcement learning features a machine learning algorithm that improves upon itself. It typically learns by trial and error to achieve a clear objective. In this Machine Learning algorithm, favorable outputs are reinforced or encouraged, whereas non-favorable outputs are discouraged. 9 Real-World Problems Solved by Machine Learning . Applications of Machine learning are many, including external (client-centric) applications such as product recommendation, customer service, and demand forecasts, and internally to help businesses improve products or speed up manual and time-consuming processes. Machine learning algorithms are typically used in areas where the solution requires continuous improvement post-deployment. Adaptable machine learning solutions are incredibly dynamic and are adopted by companies across verticals. Here we are discussing nine Machine Learning use cases –     1. Identifying Spam . Spam identification is one of the most basic applications of machine learning. Most of our email inboxes also have an unsolicited, bulk, or spam inbox, where our email provider automatically filters unwanted spam emails.  But how do they know that the email is spam? They use a trained Machine Learning model to identify all the spam emails based on common characteristics such as the email, subject, and sender content.  If you look at your email inbox carefully, you will realize that it is not very hard to pick out spam emails because they look very different from real emails. Machine learning techniques used nowadays can automatically filter these spam emails in a very successful way.  Spam detection is one of the best and most common problems solved by Machine Learning. Neural networks employ content-based filtering to classify unwanted emails as spam. These neural networks are quite similar to the brain, with the ability to identify spam emails and messages.     2. Making Product Recommendations . Recommender systems are one of the most characteristic and ubiquitous machine learning use cases in day-to-day life. These systems are used everywhere by search engines, e-commerce websites (Amazon), entertainment platforms (Google Play, Netflix), and multiple web & mobile apps. Prominent online retailers like Amazon and eBay often show a list of recommended products individually for each of their consumers. These recommendations are typically based on behavioral data and parameters such as previous purchases, item views, page views, clicks, form fill-ins, purchases, item details (price, category), and contextual data (location, language, device), and browsing history.   These recommender systems allow businesses to drive more traffic, increase customer engagement, reduce churn rate, deliver relevant content and boost profits. All such recommended products are based on a machine learning model’s analysis of customer’s behavioral data. It is an excellent way for online retailers to offer extra value and enjoy various upselling opportunities using machine learning.     3. Customer Segmentation . Customer segmentation, churn prediction and customer lifetime value (LTV) prediction are the main challenges faced by any marketer. Businesses have a huge amount of marketing relevant data from various sources such as email campaigns, website visitors and lead data. Using data mining and machine learning, an accurate prediction for individual marketing offers and incentives can be achieved. Using ML, savvy marketers can eliminate guesswork involved in data-driven marketing. For example, given the pattern of behavior by a user during a trial period and the past behaviors of all users, identifying chances of conversion to paid version can be predicted. A model of this decision problem would allow a program to trigger customer interventions to persuade the customer to convert early or better engage in the trial.     4. Image & Video Recognition . Advances in deep learning (a subset of machine learning) have stimulated rapid progress in image & video recognition techniques over the past few years. They are used for multiple areas, including object detection, face recognition, text detection, visual search, logo and landmark detection, and image composition. Since machines are good at processing images, Machine Learning algorithms can train Deep Learning frameworks to recognize and classify images in the dataset with much more accuracy than humans.  Similar to image recognition, companies such as Shutterstock, eBay, Salesforce, Amazon, and Facebook use Machine Learning for video recognition where videos are broken down frame by frame and classified as individual digital images.     5. Fraudulent Transactions . Fraudulent banking transactions are quite a common occurrence today. However, it is not feasible (in terms of cost involved and efficiency) to investigate every transaction for fraud, translating to a poor customer service experience. Machine Learning in finance can automatically build super-accurate predictive maintenance models to identify and prioritize all kinds of possible fraudulent activities. Businesses can then create a data-based queue and investigate the high priority incidents. It allows you to deploy resources in an area where you will see the greatest return on your investigative investment. Further, it also helps you optimize customer satisfaction by protecting their accounts and not challenging valid transactions. Such fraud detection using machine learning can help banks and financial organizations save money on disputes/chargebacks as one can train Machine Learning models to flag transactions that appear fraudulent based on specific characteristics.     6. Demand Forecasting . The concept of demand forecasting is used in multiple industries, from retail and e-commerce to manufacturing and transportation. It feeds historical data to Machine Learning algorithms and models to predict the number of products, services, power, and more. It allows businesses to efficiently collect and process data from the entire supply chain, reducing overheads and increasing efficiency. ML-powered demand forecasting is very accurate, rapid, and transparent. Businesses can generate meaningful insights from a constant stream of supply/demand data and adapt to changes accordingly.      7. Virtual Personal Assistant . From Alexa and Google Assistant to Cortana and Siri, we have multiple virtual personal assistants to find accurate information using our voice instruction, such as calling someone, opening an email, scheduling an appointment, and more. These virtual assistants use Machine Learning algorithms for recording our voice instructions, sending them over the server to a cloud, followed by decoding them using Machine Learning algorithms and acting accordingly.     8. Sentiment Analysis . Sentiment analysis is one of the beneficial and real-time machine learning applications that help determine the emotion or opinion of the speaker or the writer.  For instance, if you’ve written a review, email, or any other form of a document, a sentiment analyzer will be able to assess the actual thought and tone of the text. This sentiment analysis application can be used to analyze decision-making applications, review-based websites, and more.     9. Customer Service Automation . Managing an increasing number of online customer interactions has become a pain point for most businesses. It is because they simply don’t have the customer support staff available to deal with the sheer number of inquiries they receive daily. Machine learning algorithms have made it possible and super easy for chatbots and other similar automated systems to fill this gap. This application of machine learning enables companies to automate routine and low priority tasks, freeing up their employees to manage more high-level customer service tasks.  Further, Machine Learning technology can access the data, interpret behaviors and recognize the patterns easily. This could also be used for customer support systems that can work identical to a real human being and solve all of the customers’ unique queries. The Machine Learning models behind these voice assistants are trained on human languages and variations in the human voice because it has to efficiently translate the voice to words and then make an on-topic and intelligent response. If implemented the right way, problems solved by machine learning can streamline the entire process of customer issue resolution and offer much-needed assistance along with enhanced customer satisfaction. Wrapping Up . As advancements in machine learning evolve, the range of use cases and applications of machine learning too will expand. To effectively navigate the business issues in this new decade, it’s worth keeping an eye on how machine learning applications can be deployed across business domains to reduce costs, improve efficiency and deliver better user experiences. However, to implement machine learning accurately in your organization, it is imperative to have a trustworthy partner with deep-domain expertise. At Maruti Techlabs, we offer advanced machine learning services that involve understanding the complexity of varied business issues, identifying the existing gaps, and offering efficient and effective tech solutions to manage these challenges. If you wish to learn more about how machine learning solutions can increase productivity and automate business processes for your business, get in touch with us. Continue Reading Looking for a FREE consultation? Let’s connect. We’d love to hear from you. Contact Us × Maruti Techlabs is a leading enterprise software development services provider in India. We area team of passionate, purpose-led individuals that obsess over creating innovative solutions toaddress our clients' challenges and deliver unparalleled value. Home Products Services © Maruti TechLabs Pvt Ltd About Us Resources Privacy Policy Careers Connect with us We are a software company and a community of passionate, purpose-led individuals. We think disruptively to deliver technology to address our clients' toughest challenges, all while seeking to revolutionize the IT industry and create positive social change. Home Products Services About Us Resources Careers Privacy Policy Connect with us © Maruti TechLabs Pvt Ltd How Machine Learning can boost your predictive analytics How can Artificial Intelligence help FinTech companies? We use cookies to improve your browsing experience. Learn about our Privacy policyOk Scroll to top
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                            [description] => What is machine learning? And what is it used for? We explore this fascinating technology, how it’s used, the type of careers on offer, and the skills you’ll need to get started
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