Machine learning for dummies pdf download

Machine learning for dummies pdf download

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Download Machine Learning for Dummies by John Paul Mueller in PDF EPUB format complete free. Brief Summary of Book: Machine Learning for Dummies by John Paul Mueller Here is a quick description and cover image of book Machine Learning for Dummieswritten by John Paul Muellerwhich was published in — Creating new machine learning tasks Machine learning algorithms aren’t creative, which means that humans must provide the creativity that improves machine learning. Even algorithms that 24/04/ · dummies download VeronicaCarroll You also want an ePaper? Increase the reach of your titles YUMPU automatically turns print PDFs into web optimized ePapers that 31/05/ · Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical 17/07/ · Download Machine Learning for Dummies by John and Luca Book PDF Click on the below link to download Machine Learning for Dummies by John and Luca Book in PDF. ... read more

Illustrating Hyper-Parameters. Classifying and Estimating with SVM. Growing a forest of trees. Understanding the importance measures. Working with Almost Random Guesses. Bagging predictors with Adaboost. Boosting Smart Predictors. Meeting again with gradient descent. Averaging Different Predictors. CHAPTER CHAPTER Classifying Images. Extracting Visual Features. Recognizing Faces Using Eigenfaces. Classifying Images. Understanding How Machines Read. Processing and enhancing text. Scraping textual datasets from the web. Handling problems with raw text.

Using Scoring and Classification. Performing classification tasks. Analyzing reviews from e-commerce. Downloading Rating Data. Trudging through the MovieLens dataset. Navigating through anonymous web data. Encountering the limits of rating data. Leveraging SVD. Considering the origins of SVD. Understanding the SVD connection. Seeing SVD in action. Machine Learning For Dummies provides you with a view of machine learning in the real world and exposes you to the amazing feats you really can perform using this technology. Even though the tasks that you perform using machine learning may seem a bit mundane when compared to the movie version, by the time you finish this book, you realize that these mundane tasks have the power to impact the lives of everyone on the planet in nearly every aspect of their daily lives.

In short, machine learning is an incredible technology — just not in the way that some people have imagined. Instead of dealing with abstractions, you see the concrete results of using specific algorithms to interact with big data in particular ways to obtain a certain, useful result. The emphasis is on useful because machine learning has the power to perform a wide array of tasks in a manner never seen before. Part of the emphasis of this book is on using the right tools. This book uses both Python and R to perform various tasks. These two languages have special features that make them particularly useful in a machine learning setting. Likewise, R provides an ease of use that few languages can match. Machine Learning For Dummies helps you understand that both languages have their role to play and gives examples of when one language works a bit better than the other to achieve the goals you have in mind.

Introduction 1 You also discover some interesting techniques in this book. After you read this book, you finally have a basis on which to build your knowledge and go even further in using machine learning to perform tasks in your specific field. At the beginning, you find complete installation instructions for both RStudio and Anaconda, which are the Integrated Development Environments IDEs used for this book. In addition, quick primers with references help you understand the basic R and Python programming that you need to perform. Although most assumptions are indeed foolish, we made certain assumptions to provide a starting point for the book. Chapter 4 does, however, provide RStudio installation instructions, and Chapter 6 tells you how to install Anaconda. You really do need to know how to install applications, use applications, and generally work with your chosen platform before you begin working with this book.

Yes, you see lots of examples of complex math, but the emphasis is on helping you use R, Python, and machine learning to perform analysis tasks rather than learn math theory. However, you do get explanations of many of the algorithms used in the book so that you can understand how the algorithms work. Chapters 1 and 2 guide you through a better understanding of precisely what you need to know in order to use this book successfully. This book also assumes that you can access items on the Internet. Sprinkled throughout are numerous references to online material that will enhance your learning experience.

However, these added sources are useful only if you actually find and use them. Icons Used in This Book As you read this book, you encounter icons in the margins that indicate material of interest or not, as the case may be. The tips in this book are time-saving techniques or pointers to resources that you should try so that you can get the maximum benefit from R or Python, or in performing machine learning-related tasks. Otherwise, you might find that your application fails to work as expected, you get incorrect answers from seemingly bulletproof equations, or in the worst-case scenario you lose data. Whenever you see this icon, think advanced tip or technique.

You might find these tidbits of useful information just too boring for words, or they could contain the solution you need to get a program running. Skip these bits of information whenever you like. This text usually contains an essential process or a bit of information that you must know to work with R or Python, or to perform machine learning—related tasks successfully. RStudio and Anaconda come equipped to perform a wide range of general tasks. However, machine learning also requires that you perform some specific tasks, which means downloading additional support from the web.

This icon indicates that the following text contains a reference to an online source that you need to know about, and that you need to pay particular attention to so that you install everything needed to make the examples work. We provide online content to make this book more flexible and better able to meet your needs. That way, as we receive email from you, we can address questions and tell you how updates to R, Python, or their associated add-ons affect book content. You do? Well, a cheat sheet is sort of like that. It provides you with some special notes about tasks that you can do with R, Python, RStudio, Anaconda, and machine learning that not every other person knows. It contains really neat information such as finding the algorithms you commonly need for machine learning. For example, we might not have seen an upcoming change when we looked into our crystal ball during the writing of this book.

Who really wants to type all the code in the book and reconstruct all those plots manually? Most readers prefer to spend their time actually working with R, Python, performing machine learning tasks, and 4 Machine Learning For Dummies seeing the interesting things they can do, rather than typing. Fortunately for you, the examples used in the book are available for download, so all you need to do is read the book to learn machine learning usage techniques. Make sure to read about both R and Python because the book uses both languages as needed for the examples. If you already have RStudio installed, you can skim Chapter 4. Likewise, if you already have Anaconda installed, you can skim Chapter 6. To use this book, you must install R version 3. The Python version we use is 2. Readers who have some exposure to both R and Python, and have the appropriate language versions installed, can save reading time by moving directly to ­Chapter 8.

You can always go back to earlier chapters as necessary when you have questions. However, you do need to understand how each technique works before moving to the next one. Every technique, coding example, and procedure has important ­lessons for you, and you could miss vital content if you start skipping too much information. Introduction 5 1 Introducing How Machines Learn IN THIS PART. Talking to your smartphone is both fun and helpful to find out things like the location of the best sushi restaurant in town or to discover how to get to the concert hall.

As you talk to your smartphone, it learns more about the way you talk and makes fewer mistakes in understanding your requests. The capability of your smartphone to learn and interpret your particular way of speaking is an example of an AI, and part of the technology used to make it happen is machine learning. You likely make limited use of machine learning and AI all over the place today without really thinking about it. For example, the capability to speak to devices and have them actually do what you intend is an example of machine learning at work. Likewise, recommender systems, such as those found on Amazon, help you make purchases based on criteria such as CHAPTER 1 Getting the Real Story about AI 9 previous product purchases or products that complement a current choice. The use of both AI and machine learning will only increase with time.

In this chapter, you delve into AI and discover what it means from several perspectives, including how it affects you as a consumer and as a scientist or engineer. Machine learning is definitely different from AI, even though the two are related. Moving beyond the Hype As any technology becomes bigger, so does the hype, and AI certainly has a lot of hype surrounding it. For one thing, some people have decided to engage in fear mongering rather than science. The reality is that you interact with AI and machine learning in far more mundane ways already. Part of the reason you need to read this chapter is to get past the hype and discover what AI can do for you today. You may also have heard machine learning and AI used interchangeably.

This chapter helps you understand the relationship between machine learning and AI so that you can better understand how this book helps you move into a technology that used to appear only within the confines of science fiction novels. Machine learning and AI both have strong engineering components. That is, you can quantify both technologies precisely based on theory substantiated and tested explanations rather than simply hypothesis a suggested explanation for a phenomenon. In addition, both have strong science components, through which people test concepts and create new ideas of how expressing the thought process might be possible.

Finally, machine learning also has an artistic component, and this is where a talented scientist can excel. In some cases, AI and machine learning both seemingly defy logic, and only the true artist can make them work as expected. The bottom line is that the focus of this book is on helping you understand machine learning in a positive light. Dreaming of Electric Sheep Androids a specialized kind of robot that looks and acts like a human, such as Data in Star Trek and some types of humanoid robots a kind of robot that has human characteristics but is easily distinguished from a human, such as C-3PO in Star Wars have become the poster children for AI. They present computers in a form that people can anthropomorphize. Science fiction authors, such as Philip K. Dick, have long predicted such an occurrence, and it seems all too possible today.

The sections that follow help you understand how close technology ­currently gets to the ideals presented by science fiction authors and the movies. Viewing videos online can help you understand that androids that are indistinguishable from humans are nowhere near any sort of reality today. Understanding the history of AI and ­machine learning There is a reason, other than anthropomorphization, that humans see the ultimate AI as one that is contained within some type of android. Ever since the ancient Greeks, humans have discussed the possibility of placing a mind inside a mechanical body. htm , makes it quite likely that their dreams were built on more than just fantasy. AI is built on the hypothesis that mechanizing thought is possible. During the first millennium, Greek, Indian, and Chinese philosophers all worked on ways to perform this task.

As early as the seventeenth century, Gottfried Leibniz, Thomas Hobbes, and René Descartes discussed the potential for rationalizing all thought as simply math symbols. Of course, the complexity of the problem eluded them and still eludes us today, despite the advances you read about in Part 3 of the book. The point is that the vision for AI has been around for an incredibly long time, but the implementation of AI is relatively new. In this paper, Turing explored the idea of how to determine whether machines can think. Of course, this paper led to the Imitation Game involving three players. Player A is a computer and Player B is a human. The problem that scientists are trying to solve with AI is incredibly complex. However, the early optimism of the s and s led scientists to believe that the world would produce intelligent machines in as little as 20 years. After all, machines were doing all sorts of amazing things, such as playing complex games.

AI currently has its greatest success in areas such as logistics, data mining, and medical diagnosis. Exploring what machine learning can do for AI Machine learning relies on algorithms to analyze huge datasets. What machine learning can do is perform predictive analytics far faster than any human can. As a result, machine learning can help humans work more efficiently. The current state of AI, then, is one of performing analysis, but humans must still consider the implications of that analysis — making the required moral and ethical decisions. The essence of the matter is that machine learning provides just the learning part of AI, and that part is nowhere near ready to create an AI of the sort you see in films.

Nothing supports this view of machine learning. The same phenomenon occurs when people assume that a computer is purposely causing problems for them. A true AI will eventually occur when computers can finally emulate the clever combination used by nature: »» Genetics: Slow learning from one generation to the next »» Teaching: Fast learning from organized sources »» Exploration: Spontaneous learning through media and interactions with others Considering the goals of machine learning At present, AI is based on machine learning, and machine learning is essentially different from statistics. Yes, machine learning has a statistical basis, but it makes some different assumptions than statistics do because the goals are different. CHAPTER 1 Getting the Real Story about AI 13 Table lists some features to consider when comparing AI and machine learning to statistics.

TABLE Comparing Machine Learning to Statistics Technique Machine Learning Statistics Data handling Works with big data in the form of networks and graphs; raw data from sensors or the web text is split into training and test data. Models are used to create predictive power on small samples. Data input The data is sampled, randomized, and transformed to maximize accuracy scoring in the prediction of out of sample or completely new examples. Parameters interpret real world phenomena and provide a stress on magnitude. Result Probability is taken into account for comparing what could be the best guess or decision.

The output captures the variability and uncertainty of parameters. Assumptions The scientist learns from the data. The scientist assumes a certain output and tries to prove it. Distribution The distribution is unknown or ignored before learning from data. The scientist assumes a well-defined distribution. Fitting The scientist creates a best fit, but generalizable, model. The result is fit to the present data distribution. Defining machine learning limits based on hardware Huge datasets require huge amounts of memory.

When you have huge amounts of data and memory, you must also have processors with multiple cores and high speeds. One of the problems that scientists are striving to solve is how to use existing hardware more efficiently. With this in mind, investments in better hardware also require investments in better science. This book considers some of the following issues as part of making your machine learning experience better: »» Obtaining a useful result: As you work through the book, you discover that you need to obtain a useful result first, before you can refine it.

In addition, sometimes tuning an algorithm goes too far and the result becomes quite fragile and possibly useless outside a specific dataset. The question might be wrong, which means that even the best hardware will never find the answer. A scientist uses intuition to create a starting point for discovering the answer to a question. Failure is more common than success when working through a machine learning experience. Your intuition adds the art to the machine learning experience, but sometimes intuition is wrong and you have to revisit your assumptions. When you begin to realize the importance of environment to machine learning, you can also begin to understand the need for the right hardware and in the right balance to obtain a desired result.

The current state-of-the-art systems actually rely on Graphical Processing Units GPUs to perform machine learning tasks. Relying on GPUs does speed the machine learning process considerably. Overcoming AI Fantasies As with many other technologies, AI and machine learning both have their ­fantasy or fad uses. For example, some people are using machine learning to ­create Picasso-like art from photos. Of course, the problems with such use are many. The end of the article points out that the computer can only copy an existing style at this stage — not create an entirely new style of its own. The following sections discuss AI and machine learning fantasies of various sorts. CHAPTER 1 Getting the Real Story about AI 15 Discovering the fad uses of AI and machine learning AI is entering an era of innovation that you used to read about only in science ­fiction.

It can be hard to determine whether a particular AI use is real or simply the dream child of a determined scientist. To make the future uses of AI and machine learning match the concepts that science fiction has presented over the years, real-world programmers, data scientists, and other stakeholders need to create tools. Chapter 8 explores some of the new tools that you might use when working with AI and machine learning, but these tools are still rudimentary. In order for the fad uses for AI and machine learning to become real-world uses, developers, data scientists, and others need to continue building real-world tools that may be hard to imagine at this point.

Considering the true uses of AI and ­machine learning You find AI and machine learning used in a great many applications today. In fact, you might be surprised to find that many devices in your home already make use of both technologies. Both technologies definitely appear in your car and most especially in the workplace. Here are just a few of the ways in which you might see AI used: »» Fraud detection: You get a call from your credit card company asking whether you made a particular purchase. com purchase using your card. For example, the same set of symptoms could indicate more than one problem.

A problem with some types of automation today is that an unexpected event, such as an object in the wrong place, can actually cause the automation to stop. Adding AI to the automation can allow the automation to handle unexpected events and continue as if nothing happened. The automation is good enough to follow scripts and use various resources to handle the vast majority of your questions. For example, many automatic braking systems rely on AI to stop the car based on all the inputs that a vehicle can provide, such as the direction of a skid. Every ounce of power is used precisely as needed to provide the desired services.

You can find AI used in many other ways. Here are a few uses for machine learning that you might not associate with an AI: »» Access control: In many cases, access control is a yes or no proposition. An employee smartcard grants access to a resource much in the same way that people have used keys for centuries. By using machine learning, you can determine whether an employee should gain access to a resource based on role and need. For example, an employee can gain access to a training room when the training reflects an employee role. Unfortunately, many animals get hit by ships each year. A machine learning algorithm could allow ships to avoid animals by learning the sounds and characteristics of both the animal and the ship. Machine learning allows an application to determine waiting times based on staffing levels, staffing load, complexity of the problems the staff is trying to solve, availability of resources, and so on.

Being useful; being mundane Even though the movies make it sound like AI is going to make a huge splash, and you do sometimes see some incredible uses for AI in real life, the fact of the matter is that most uses for AI are mundane, even boring. Part 5 of this book provides you with real-world examples of this same sort of analysis. The act of performing this analysis is dull when compared to other sorts of AI activities, but the benefits are that Verizon saves money performing the analysis using R, and the results are better as well. In addition, Python developers see Chapters 6 and 7 for Python language details have a huge array of libraries available to make machine learning easy. The results of these competitions often appear later as part of products that people actually use.

html for the top 20 Python libraries in use today. To prove successful, a machine learning session must use an appropriate algorithm to achieve a desired result. In addition, the data must lend itself to analysis using the desired algorithm, or it requires a careful preparation by scientists. AI encompasses many other disciplines to simulate the thought process successfully. In addition to machine learning, AI normally includes »» Natural language processing: The act of allowing language input and putting it into a form that a computer can use. In fact, you might be surprised to find that the number of disciplines required to create an AI is huge.

Consequently, this book exposes you to only a portion of what an AI contains. However, even the machine learning portion of the picture can become complex because understanding the world through the data inputs that a computer receives is a complex task. Just think about all the decisions that you constantly make without thinking about them. For example, just the concept of seeing something and knowing whether you can interact successfully with it can become a complex task. CHAPTER 1 Getting the Real Story about AI 19 Considering AI and Machine Learning Specifications As scientists continue to work with a technology and turn hypotheses into theories, the technology becomes related more to engineering where theories are implemented than science where theories are created.

As the rules governing a technology become clearer, groups of experts work together to define these rules in written form. The result is specifications a group of rules that everyone agrees upon. The basis for machine learning is math. Algorithms determine how to interpret big data in specific ways. The math basics for machine learning appear in Part 3 of the book. You discover that algorithms process input data in specific ways and create predictable outputs based on the data patterns. The reason you need AI and machine learning is to decipher the data in such a manner to be able to see the patterns in it and make sense of them. You see the specifications detailed in Part 4 in the form of algorithms used to perform specific tasks. When you get to Part 5, you begin to see the reason that everyone agrees to specific sets of rules governing the use of algorithms to perform tasks.

Professionals implement algorithms using languages that work best for the task. Defining the Divide between Art and Engineering The reason that AI and machine learning are both sciences and not engineering disciplines is that both require some level of art to achieve good results. The artistic element of machine learning takes many forms. For example, you must consider how the data is used. Some data acts as a baseline that trains an algorithm 20 PART 1 Introducing How Machines Learn to achieve specific results. The remaining data provides the output used to understand the underlying patterns. No specific rules governing the balancing of data exist; the scientists working with the data must discover whether a specific balance produces optimal output.

Cleaning the data also lends a certain amount of artistic quality to the result. The manner in which a scientist prepares the data for use is important. Some tasks, such as removing duplicate records, occur regularly. However, a scientist may also choose to filter the data in some ways or look at only a subset of the data. As a result, the cleaned dataset used by one scientist for machine learning tasks may not precisely match the cleaned dataset used by another. You can also tune the algorithms in certain ways or refine how the algorithm works. Again, the idea is to create output that truly exposes the desired patterns so that you can make sense of the data.

The answer to that question is important if the robot must avoid some elements to keep on track or to achieve specific goals. When working in a machine learning environment, you also have the problem of input data to consider. The characteristics of the microphones differ, yet the result of interpreting the vocal commands provided by the user must remain the same. Likewise, environmental noise changes the input quality of the vocal command, and the smartphone can experience certain forms of electromagnetic interference. Clearly, the variables that a designer faces when creating a machine learning environment are both large and complex. The art behind the engineering is an essential part of machine learning.

The experience that a scientist gains in working through data problems is essential because it provides the means for the scientist to add values that make the algorithm work better. A finely tuned algorithm can make the difference between a robot successfully threading a path through obstacles and hitting every one of them. CHAPTER 1 Getting the Real Story about AI 21 IN THIS CHAPTER Understanding the essentials of big data Locating big data sources Considering how statistics and big data work together in machine learning Defining the role of algorithms in machine learning Determining how training works with algorithms in machine learning Chapter 2 Learning in the Age of Big Data C omputers manage data through applications that perform tasks using algorithms of various sorts.

A simple definition of an algorithm is a systematic set of operations to perform on a given data set — essentially a procedure. The four basic data operations are Create, Read, Update, and Delete CRUD. This set of operations may not seem complex, but performing these essential tasks is the basis of everything you do with a computer. As the dataset becomes larger, the computer can use the algorithms found in an application to perform more work. The use of immense datasets, known as big data, enables a computer to perform work based on pattern recognition in a nondeterministic manner. In short, to ­create a computer setup that can learn, you need a dataset large enough for the algorithms to manage in a manner that allows for pattern recognition, and this pattern recognition needs to use a simple subset to make predictions statistical analysis of the dataset as a whole.

CHAPTER 2 Learning in the Age of Big Data 23 Big data exists in many places today. Obvious sources are online databases, such as those created by vendors to track consumer purchases. However, you find many non-obvious data sources, too, and often these non-obvious sources provide the greatest resources for doing something interesting. Finding appropriate sources of big data lets you create machine learning scenarios in which a machine can learn in a specified manner and produce a desired result. Statistics, one of the methods of machine learning that you consider in this book, is a method of describing problems using math. By combining big data with statistics, you can create a machine learning environment in which the machine considers the probability of any given event.

However, saying that statistics is the only machine learning method is incorrect. This chapter also introduces you to the other forms of machine learning currently in place. Algorithms determine how a machine interprets big data. The algorithm used to perform machine learning affects the outcome of the learning process and, therefore, the results you get. This chapter helps you understand the five main techniques for using algorithms in machine learning. Before an algorithm can do much in the way of machine learning, you must train it. The training process modifies how the algorithm views big data. The final section of this chapter helps you understand that training is actually using a subset of the data as a method for creating the patterns that the algorithm needs to recognize specific cases from the more general cases that you provide as part of the training. Defining Big Data Big data is substantially different from being just a large database. Yes, big data implies lots of data, but it also includes the idea of complexity and depth.

A big data source describes something in enough detail that you can begin working with that data to solve problems for which general programming proves inadequate. The data source contains many variables — all of which affect the vehicle in some way. Traditional programming might be able to crunch all the numbers, but not in real time. The processing must prove timely so that the car can avoid the wall. Big data can really become quite big. What you might end up with is a raw dataset with input that exceeds Mbps. Processing that much data is incredibly hard. Part of the problem right now is determining how to control big data. Currently, the attempt is to log everything, which produces a massive, detailed dataset. As this book progresses, you discover techniques that help control both the size and the organization of big data so that the data becomes useful in making predictions.

The acquisition of big data can also prove daunting. In most cases, developers try to store the dataset in memory to allow fast processing. Using a hard drive to store the data would prove too costly, time-wise. When thinking about big data, you also consider anonymity. Big data presents privacy concerns. Personal data has no place in such an environment. Part of what defined big data as big is the fact that a human can learn something from it, but the sheer magnitude of the dataset makes recognition of the patterns impossible or would take a really long time to accomplish. Machine learning helps humans make sense and use of big data. Considering the Sources of Big Data Before you can use big data for a machine learning application, you need a source of big data. The fact of the matter is that your corporate databases might not even contain particularly useful data for a specific need. The following sections describe locations you can use to obtain additional big data.

CHAPTER 2 Learning in the Age of Big Data 25 Building a new data source To create viable sources of big data for specific needs, you might find that you actually need to create a new data source. Developers built existing data sources around the needs of the client-server architecture in many cases, and these sources may not work well for machine learning scenarios because they lack the required depth being optimized to save space on hard drives does have disadvantages. With this in mind, the following sections describe some interesting new sources for big data. Obtaining data from public sources Governments, universities, nonprofit organizations, and other entities often maintain publicly available databases that you can use alone or combined with other databases to create big data for machine learning.

For example, you can combine several Geographic Information Systems GIS to help create the big data required to make decisions such as where to put new stores or factories. The machine learning algorithm can take all sorts of information into account — everything from the amount of taxes you have to pay to the elevation of the land which can contribute to making your store easier to see. In addition, many of the organizations that created them maintain these sources in nearly perfect condition because the organization has a mandate, uses the data to attract income, or uses the data internally. When obtaining public source data, you need to consider a number of issues to ensure that you actually get something useful. Here are some of the criteria you should think about when making a decision: »» The cost, if any, of using the data source »» The formatting of the data source »» Access to the data source which means having the proper infrastructure in place, such as an Internet connection when using Twitter data »» Permission to use the data source some data sources are copyrighted »» Potential issues in cleaning the data to make it useful for machine learning Obtaining data from private sources You can obtain data from private organizations such as Amazon and Google, both of which maintain immense databases that contain all sorts of useful information.

In this case, you should expect to pay for access to the data, especially when used in a commercial setting. You may not be allowed to download the data to your 26 PART 1 Introducing How Machines Learn personal servers, so that restriction may affect how you use the data in a machine learning environment. For example, some algorithms work slower with data that they must access in small pieces. The biggest advantage of using data from a private source is that you can expect better consistency. The data is likely cleaner than from a public source. In addition, you usually have access to a larger database with a greater variety of data types.

Of course, it all depends on where you get the data. One of the new job types that you can expect to create is people who massage data to make it better suited for machine learning — including the addition of specific information types such as tags. Machine learning will have a significant effect on your business. html describes some of the ways in which you can expect machine learning to change how you do business. One of the points in this article is that machine learning typically works on 80 percent of the data. In 20 percent of the cases, you still need humans to take over the job of deciding just how to react to the data and then act upon it. Also important to consider is that you need more humans at the outset until the modifications they make train the algorithm to understand what sorts of changes to make to the data.

Using existing data sources Your organization has data hidden in all sorts of places. The problem is in recognizing the data as data. For example, you may have sensors on an assembly line that track how products move through the assembly process and ensure that the assembly line remains efficient. Those same sensors can potentially feed information into a machine learning scenario because they could provide inputs on how product movement affects customer satisfaction or the price you pay for postage. The idea is to discover how to create mashups that present existing data as a new kind of data that lets you do more to make your organization work well.

CHAPTER 2 Learning in the Age of Big Data 27 Big data can come from any source, even your email. A recent article discusses how Google uses your email to create a list of potential responses for new emails. Instead of having to respond to every email individually, you can simply select a canned response at the bottom of the page. By the time you complete the video, you begin to understand that many uses of machine learning are already in place and users already take them for granted or have no idea that the application is even present. This training process ensures that the algorithm reacts correctly to the data it receives after the training is over. Of course, you also need to test the algorithm to determine whether the training is a success. In many cases, the book helps you discover ways to break a data source into training and testing data components in order to achieve the desired result.

Then, after training and testing, the algorithm can work with new data in real time to perform the tasks that you verified it can perform. In some cases, you might not have enough data at the outset for both training the essential initial test and testing. When this happens, you might need to create a test setup to generate more data, rely on data generated in real time, or create the test data source artificially. You can also use similar data from existing sources, such as a public or private database. The point is that you need both training and testing data that will produce a known result before you unleash your algorithm into the real world of working with uncertain data. For example, when you read Statistics vs. Machine Learning, fight! The fact is that statistics and machine learning have a lot in common and that statistics represents one of the five tribes schools of thought that make machine learning feasible.

The five tribes are »» Symbolists: The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems. This group relies on backpropagation to solve problems. This group relies on genetic programming to solve problems. This group relies on probabilistic inference to solve problems. This group relies on kernel machines to solve problems. The ultimate goal of machine learning is to combine the technologies and strategies embraced by the five tribes to create a single algorithm the master algorithm that can learn anything.

Of course, achieving that goal is a long way off. This book follows the Bayesian tribe strategy, for the most part, in that you solve most problems using some form of statistical analysis. You do see strategies embraced by other tribes described, but the main reason you begin with statistics is that the technology is already well established and understood. In fact, many elements of statistics qualify more as engineering in which theories are implemented than science in which theories are created. The next section of the chapter delves deeper into the five tribes by viewing the kinds of algorithms each tribe uses. Understanding the role of algorithms in machine learning is essential to defining how machine learning works. CHAPTER 2 Learning in the Age of Big Data 29 Understanding the Role of Algorithms Everything in machine learning revolves around algorithms. An algorithm is a procedure or formula used to solve a problem.

The problem domain affects the kind of algorithm needed, but the basic premise is always the same — to solve some sort of problem, such as driving a car or playing dominoes. In the first case, the problems are complex and many, but the ultimate problem is one of getting a passenger from one place to another without crashing the car. Likewise, the goal of playing dominoes is to win. The following sections discuss algorithms in more detail. Defining what algorithms do An algorithm is a kind of container. It provides a box for storing a method to solve a particular kind of a problem.

Algorithms process data through a series of welldefined states. The states need not be deterministic, but the states are defined nonetheless. The goal is to create an output that solves a problem. In some cases, the algorithm receives inputs that help define the output, but the focus is always on the output. Algorithms must express the transitions between states using a well-defined and formal language that the computer can understand. In processing the data and solving the problem, the algorithm defines, refines, and executes a function. The function is always specific to the kind of problem being addressed by the algorithm.

Considering the five main techniques As described in the previous section, each of the five tribes has a different technique and strategy for solving problems that result in unique algorithms. Combining these algorithms should lead eventually to the master algorithm that will be able to solve any given problem. The following sections provide an overview of the five main algorithmic techniques. Symbolic reasoning The term inverse deduction commonly appears as induction. In symbolic reasoning, deduction expands the realm of human knowledge, while induction raises the level of human knowledge. Induction commonly opens new fields of exploration, while deduction explores those fields.

However, the most important consideration is that induction is the science portion of this type of reasoning, while deduction is the engineering. The two strategies work hand in hand to solve problems by first opening a field of potential exploration to solve the problem and then exploring that field to determine whether it does, in fact, solve it. When thinking about induction, you would say that the tree is green and that the tree is also alive; therefore, green trees are alive. Induction provides the answer to what knowledge is missing given a known input and output. Essentially, each of the neurons created as an algorithm that models the real-world counterpart solves a small piece of the problem, and the use of many neurons in parallel solves the problem as a whole. The use of backpropagation, or backward propagation of errors, seeks to determine the conditions under which errors are removed from networks built to resemble the human neurons by changing the weights how much a particular input figures into the result and biases which features are selected of the network.

The goal is to continue changing the weights and biases until such time as the actual output matches the target output. At this point, the artificial neuron fires and passes its solution along to the next neuron in line. The solution created by just one neuron is only part of the whole solution. Each neuron passes information to the next neuron in line until the group of neurons creates a final output. Evolutionary algorithms that test variation The evolutionaries rely on the principles of evolution to solve problems. A fitness function determines the viability of each function in solving a problem. Using a tree structure, the solution method looks for the best solution based on function output. The winner of each level of evolution gets to build the next-level functions. The idea is that the next level will get closer to solving the problem but may not solve it completely, which means that another level is needed.

This particular tribe relies heavily on recursion and languages that strongly support recursion to solve problems. An interesting output of this strategy has been algorithms that evolve: One generation of algorithms actually builds the next generation. Bayesian inference The Bayesians use various statistical methods to solve problems. Given that statistical methods can create more than one apparently correct solution, the choice of a function becomes one of determining which function has the highest probability of succeeding. For example, when using these techniques, you can accept a CHAPTER 2 Learning in the Age of Big Data 31 set of symptoms as input and decide the probability that a particular disease will result from the symptoms as output.

Given that multiple diseases have the same symptoms, the probability is important because a user will see some in which a lower probability output is actually the correct output for a given circumstance. Ultimately, this tribe supports the idea of never quite trusting any hypothesis a result that someone has given you completely without seeing the evidence used to make it the input the other person used to make the hypothesis. Analyzing the evidence proves or disproves the hypothesis that it supports. One of the most recognizable outputs from this tribe is the spam filter. Systems that learn by analogy The analogyzers use kernel machines to recognize patterns in data. By recognizing the pattern of one set of inputs and comparing it to the pattern of a known output, you can create a problem solution. The goal is to use similarity to determine the best solution to a problem.

One of the most recognizable outputs from this tribe is recommender systems. For example, when you get on Amazon and buy a product, the recommender system comes up with other, related products that you might also want to buy. Defining What Training Means Many people are somewhat used to the idea that applications start with a function, accept data as input, and then provide a result. For example, a programmer might create a function called Add that accepts two values as input, such as 1 and 2. The result of Add is 3. The output of this process is a value. In the past, writing a program meant understanding the function used to manipulate data to create a given result with certain inputs. Machine learning turns this process around. In this case, you know that you have inputs, such as 1 and 2. You also know that the desired result is 3. Training provides a learner algorithm with all sorts of examples of the desired inputs and results expected from those inputs.

The learner then uses this input to create a function. In other words, training is the process whereby the learner algorithm maps a flexible function to the data. The output is typically the probability of a certain class or a numeric value. Some algorithms are general enough that they can play chess, recognize faces on Facebook, and diagnose cancer in patients. An algorithm reduces the data inputs and the expected results of those inputs to a function in every case, but the function is specific to the kind of task you want the algorithm to perform. The secret to machine learning is generalization. The goal is to generalize the output function so that it works on data beyond the training set.

For example, consider a spam filter. Your dictionary contains , words actually a small dictionary. When viewed from this perspective, training might seem impossible and learning even worse. However, to create this generalized function, the learner algorithm relies on just three components: »» Representation: The learner algorithm creates a model, which is a function that will produce a given result for specific inputs. The representation is a set of models that a learner algorithm can learn. In other words, the learner algorithm must create a model that will produce the desired results from the input data. Part of the representation is to discover which features data elements within the data source to use for the learning process. An evaluation function determines which of the models works best in creating a desired result from a set of inputs. The evaluation function scores the models because more than one model could provide the required results.

At this point, the training process searches through these models to determine which one works best. The best model is then output as the result of the training process. Much of this book focuses on representation. For example, in Chapter 14 you discover how to work with the K-Nearest Neighbor KNN algorithm. However, the training process is more involved than simply choosing a representation. All three steps come into play when performing the training process. Fortunately, you can start by focusing on representation and allow the various libraries discussed in the book to do the rest of the work for you. The algorithms used for machine learning today are still relatively basic when compared to what scientists plan to provide for the future. In addition, the data sources for machine learning today are smaller than the datasets planned for future use. In short, machine learning is in its infancy. It already performs a considerable number of tasks amazingly well, however.

This chapter looks at what might be possible in the future. It helps you understand the direction that machine learning is taking and how that direction could help entrench machine learning into every aspect of daily life. One of the issues that comes with a new technology such as machine learning is a fear that machine learning will keep people from working. Quite the contrary: Machine learning will open new occupations that people should find more exciting than working on an assembly line or flipping burgers at a restaurant. One of the goals is to provide creative and interesting work for people to do. Of course, these new jobs will require more and new kinds of training before people can perform them well. Every new technology also comes with pitfalls. The potential pitfalls of machine learning need to be taken seriously. Because this technology is in its infancy, now is the time to consider the potential pitfalls and do something about them before they materialize.

Creating Useful Technologies for the Future To survive, a technology must prove useful. For example, the Apple Lisa was an interesting and useful piece of technology that demonstrated the usefulness of the GUI to business users who had never seen one before. It solved the need to make computers friendly. The next system that Apple built, the Macintosh, did live up to the hype a bit better — yet it built on the same technology that the Lisa used. The difference is that the Macintosh developed a considerable array of hard-core adherents. Machine learning solves a considerable number of problems in a manner that other technologies would have a hard time copying. However, to become the must-have technology that everyone wants to invest in, machine learning must build that cadre of hard-core adherents. Machine learning already has some adherents, and you might already be one of them, but the technology must move into mainstream computing to make it something that everyone must have.

The following sections discuss some of the ways in which machine learning is already affecting you personally and how this use will increase in the future — making machine learning a must-have technology. Considering the role of machine learning in robots A goal of machine learning today is to create useful, in-home robots. However, real-world robots need to solve practical and important problems to attract attention. To become viable and attract funding, a technology must also amass a group of followers, and to do that, it must provide both interaction and ownership. You can actually buy a Roomba today; it serves a useful purpose; and it has attracted enough attention to make it a viable technology. The Roomba also shows what is doable at a commercial, in-home, and autonomous level today.

Yes, the Roomba is a fancy vacuum cleaner — one with built-in smarts based on simple but very effective algorithms. The Roomba can successfully navigate a home, which is a lot harder than you might think to accomplish. It can also spend more time on dirtier areas of the home. However, you still need to empty the Roomba when full; current robot technology does only so much. In each case, the robot has a specialized purpose and acts in a limited number of ways. Before robots can enter a home and work as a generalized helper, machine learning needs to solve a wealth of problems, and the algorithms need to become both more generalized and deeper thinking. Using machine learning in health care An issue that is receiving a lot of attention is the matter of elder care.

Robots will make it possible for people to remain at home yet also remain safe. Some countries are also facing a critical shortage of health care workers, and Japan is one. As a result, the country is spending considerable resources to solve the problems that robotics present. html for details. Like the Roomba, this robot can successfully navigate a home. It also allows the doctor to see and hear the patient. Creating smart systems for various needs Many of the solutions you can expect to see that employ machine learning will be assistants to humans. They perform various tasks extremely well, but these tasks CHAPTER 3 Having a Glance at the Future 37 are mundane and repetitive in nature. For example, you might need to find a restaurant to satisfy the needs of an out-of-town guest. You can waste time looking for an appropriate restaurant yourself, or you can access an AI to do it in far less time, with greater accuracy and efficiency.

Chapter 1 discusses such solutions in the form of Siri. Unlike Siri, which can answer basic questions, Nara goes a step further and makes recommendations. Chapter 21 spends a lot more time discussing this particular need. Using machine learning in industrial settings Machine learning is already playing an important part in industrial settings where the focus is on efficiency. Doing things faster, more accurately, and with fewer resources helps the bottom line and makes an organization more flexible with a higher profit margin. Fewer mistakes also help the humans working in an organization by reducing the frustration level.

You can currently see machine learning at work in »» Medical diagnosis »» Data mining »» Bioinformatics »» Speech and handwriting recognition »» Product categorization »» Inertial Measurement Unit IMU such as motion capture technology »» Information retrieval This list just scratches the surface. Machine learning is used a lot in industry today, and the number of uses will continue to increase as advanced algorithms make higher levels of learning possible. Currently, machine learning performs tasks in a number of areas that include the following: »» Analyzation: Determining what a user wants and why, and what sort of patterns behaviors, associations, responses, and so on the user exhibits when obtaining it.

Each user ends up with a customized experience that reduces frustration and improves productivity. You can see machine learning used in relatively mundane but important ways. For example, machine learning has a role in automating employee access, protecting animals, predicting emergency room wait times, identifying heart failure, predicting strokes and heart attacks, and predicting hospital readmissions. For example, you might talk to one tribe whose members tell you of the need for larger amounts of system memory and the use of GPUs to provide faster computations. Another tribe might espouse the creation of new types of processors. Learning processors, those that mimic the human brain, are all the rage for the connectionists. The point is that everyone agrees that some sort of new hardware will make machine learning easier, but the precise form this hardware will take remains to be seen.

Discovering the New Work Opportunities with Machine Learning You can find more than a few articles that discuss the loss of jobs that machine learning and its associated technologies will cause. Robots already perform a number of tasks that used to employ humans, and this usage will increase CHAPTER 3 Having a Glance at the Future 39 over time. The previous section of this chapter aided you in understanding some of the practical, real-world uses for machine learning today and helped you discover where those uses are likely to expand in the future. While reading this section, you must have also considered how those new uses could potentially cost you or a loved one a job.

Just as those workers needed to find new jobs, so people facing loss of occupation to machine learning today will need to find new jobs. In fact, you might already work for a machine and not know it. Some companies already use machine learning to analyze business processes and make them more efficient. In this case, the AI actually issues the work orders based on its analysis of the workflow — just as a human middle manager might do. The difference is that the AI is actually eight percent more efficient than the humans it replaces. Again, the point was to figure out how to replace middle management and cut a bit of the red tape.

However, a job opportunity also presents itself. Workers under the AI do perform the tasks that the AI tells them to do, but they can use their own experience and creativity in determining how to perform the task. The AI analyzes the processes that the human workers use and measures the results achieved. Any successful processes get added into the database of techniques that workers can apply to accomplish tasks. In other words, the humans are teaching the AI new techniques to make the work environment even more efficient. This is an example of how machine learning can free humans from the drudgery of the work environment. When using human middle managers, new processes often get buried in a bureaucracy of unspoken rules and ego.

The AI middle manager is designed to learn new techniques without bias, so the humans are encouraged to 40 PART 1 Introducing How Machines Learn exercise their creativity, and everyone benefits. In short, the AI, which lacks an ego to bruise, is the approachable manager that many workers have wanted all along. Working with machines People already work with machines on a regular basis — they may just not realize it. Most people recognize that the voice interaction provided with a smartphone improves with time — the more you use it, the better it gets at recognizing your voice. As the learner algorithm becomes better tuned, it becomes more efficient at recognizing your voice and obtaining the desired result.

This trend will continue. However, machine learning is used in all sorts of ways that might not occur to you. The camera is helping you perform the job of taking a picture with far greater efficiency. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. Author : David Foster Publisher: ISBN: Category : Languages : de Pages : View Book Description Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren.

Mit diesem praxisnahen Buch können Data Scientists einige der eindrucksvollsten generativen Deep-Learning-Modelle nachbilden wie z. Generative Adversarial Networks GANs , Variational Autoencoder VAEs , Encoder-Decoder- sowie World-Modelle. David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt. Die zahlreichen praktischen Beispiele und Tipps helfen dem Leser herauszufinden, wie seine Modelle noch effizienter lernen und noch kreativer werden können. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it.

The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models.

Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value.

If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Coding All-in-One For Dummies offers an ideal starting place for learning the languages that make technology go. Add coding to your skillset for your existing career, or begin the exciting transition into life as a professional developer—Dummies makes it easy.

You can do this. Author : Pratap Dangeti Publisher: Packt Publishing Ltd ISBN: Category : Computers Languages : en Pages : View Book Description Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.

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Download & View Machine Learning For blogger.com as PDF for free  · Download Machine Learning for Dummies by John Paul Mueller in PDF EPUB format complete free. Brief Summary of Book: Machine Learning for Dummies by John Paul Download Machine Learning for Dummies by John Paul Mueller in PDF EPUB format complete free. Brief Summary of Book: Machine Learning for Dummies by John Paul Mueller Here is a quick description and cover image of book Machine Learning for Dummieswritten by John Paul Muellerwhich was published in —  · Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical 24/04/ · dummies download VeronicaCarroll You also want an ePaper? Increase the reach of your titles YUMPU automatically turns print PDFs into web optimized ePapers that Creating new machine learning tasks Machine learning algorithms aren’t creative, which means that humans must provide the creativity that improves machine learning. Even algorithms that ... read more

The data is likely cleaner than from a public source. Your email address will not be published. Take special note of the warning provided on the CRAN site. Search icon An illustration of a magnifying glass. Locating test data sources.

Nothing supports this view of machine learning. In addition, you usually have access to a larger database with a greater variety of data types. After the process completes, you see a completion dialog box appear in its place. The output of this process is a value. x and R as a download Build and test your own models Use the latest datasets, rather than the worn out data machine learning for dummies pdf download in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world. In fact, many elements of statistics qualify more as engineering in which theories are implemented than science in which theories are created, machine learning for dummies pdf download. For this reason, you might want to scan the chapter even if you already know something about R.