Machine_Learning_in_Action（PDF 高清全文下载）

preface

After college I went to work for Intel in California and mainland China. Originally my plan was to go back to grad school after two years, but time flies when you are having fun, and two years turned into six. I realized I had to go back at that point, and I didn’t want to do night school or online learning, I wanted to sit on campus and soak up everything a university has to offer. The best part of college is not the classes you take or research you do, but the peripheral things: meeting people, going to seminars, joining organizations, dropping in on classes, and learning what you don’t know.

Sometime in 2008 I was helping set up for a career fair. I began to talk to someone from a large financial institution and they wanted me to interview for a position modeling credit risk (figuring out if someone is going to pay off their loans or not). They asked me how much stochastic calculus I knew. At the time, I wasn’t sure I knew what the word stochastic meant. They were hiring for a geographic location my body couldn’t tolerate, so I decided not to pursue it any further. But this stochastic stuff interested me, so I went to the course catalog and looked for any class being offered

with the word “stochastic” in its title. The class I found was “Discrete-time Stochastic Systems.” I started attending the class without registering, doing the homework and taking tests. Eventually I was noticed by the professor and she was kind enough to let me continue, for which I am very grateful. This class was the first time I saw probability applied to an algorithm. I had seen algorithms take an averaged value as input before, but this was different: the variance and mean were internal values in these algorithms. The course was about “time series” data where every piece of data is a regularly spaced sample. I found another course with Machine Learning in the title. In this class the data was not assumed to be uniformly spaced in time, and they covered more algorithms but with less rigor. I later realized that similar methods were also being taught in the economics, electrical engineering, and computer science departments.

In early 2009, I graduated and moved to Silicon Valley to start work as a software consultant. Over the next two years, I worked with eight companies on a very wide range of technologies and saw two trends emerge which make up the major thesis for this book: first, in order to develop a compelling application you need to do more than just connect data sources; and second, employers want people who understand theory and can also program.

pipes—except that instead of pipes, programmers connect the flow of data—and monstrous fortunes have been made doing exactly that. Let me give you an example. You could make an application that sells things online—the big picture for this would be allowing people a way to post things and to view what others have posted. To do this you could create a web form that allows users to enter data about what they are selling and then this data would be shipped off to a data store. In order for other users to see what a user is selling, you would have to ship the data out of the data store and display it appropriately. I’m sure people will continue to make money this way; however to make the application really good you need to add a level of intelligence. This intelligence could do things like automatically remove inappropriate postings, detect fraudulent transactions, direct users to things they might like, and forecast site traffic. To accomplish these objectives, you would need to apply machine learning. The end user

would not know that there is magic going on behind the scenes; to them your application “just works,” which is the hallmark of a well-built product.

a set of practical people, “doers.” The thinkers may have spent a lot of time in academia, and their day-to-day job may be pulling ideas from papers and modeling them with very high-level tools or mathematics. The doers interface with the real world by writing the code and dealing with the imperfections of a non-ideal world, such as machines that break down or noisy data. Separating thinkers from doers is a bad idea and successful organizations realize this. (One of the tenets of lean manufacturing is for the thinkers to get their hands dirty with actual doing.) When there is a limited amount of money to be spent on hiring, who will get hired more readily—the thinker or the doer? Probably the doer, but in reality employers want both. Things need to get built, but when applications call for more demanding algorithms it is useful to have someone who can read papers, pull out the idea, implement it in real code, and iterate.

I didn’t see a book that addressed the problem of bridging the gap between thinkers and doers in the context of machine learning algorithms. The goal of this book is to fill that void, and, along the way, to introduce uses of machine learning algorithms so that the reader can build better applications.

acknowledgments

This is by far the easiest part of the book to write...

First, I would like to thank the folks at Manning. Above all, I would like to thank my editor Troy Mott; if not for his support and enthusiasm, this book never would have happened. I would also like to thank Maureen Spencer who helped polish my prose in the final manuscript; she was a pleasure to work with.

Next I would like to thank Jennie Si at Arizona State University for letting me sneak into her class on discrete-time stochastic systems without registering. Also Cynthia Rudin at MIT for pointing me to the paper “Top 10 Algorithms in Data Mining,” 1 which inspired the approach I took in this book. For indirect contributions I would like to thank Mark Bauer, Jerry Barkely, Jose Zero, Doug Chang, Wayne Carter, and Tyler Neylon.

Special thanks to the following peer reviewers who read the manuscript at different stages during its development and provided invaluable feedback: Keith Kim, Franco Lombardo, Patrick Toohey, Josef Lauri, Ryan Riley, Peter Venable, Patrick Goetz, Jeroen Benckhuijsen, Ian McAllister, Orhan Alkan, Joseph Ottinger, Fred Law, Karsten Strøbæk, Brian Lau, Stephen McKamey, Michael Brennan, Kevin Jackson, John Griffin, Sumit Pal, Alex Alves, Justin Tyler Wiley, and John Stevenson.

My technical proofreaders, Tricia Hoffman and Alex Ott, reviewed the technical content shortly before the manuscript went to press and I would like to thank them both for their comments and feedback. Alex was a cold-blooded killer when it came to

reviewing my code! Thank you for making this a better book.

Thanks also to all the people who bought and read early versions of the manuscript through the MEAP early access program and contributed to the Author Onlineforum (even the trolls); this book wouldn’t be what it is without them.

I want to thank my family for their support during the writing of this book. I owe a huge debt of gratitude to my wife for her encouragement and for putting up with all the irregularities in my life during the time I spent working on the manuscript.

Finally, I would like to thank Silicon Valley for being such a great place for my wife and me to work and where we can share our ideas and passions.

about this book

This book sets out to introduce people to important machine learning algorithms. Tools and applications using these algorithms are introduced to give the reader an idea of how they are used in practice today. A wide selection of machine learning books is available, which discuss the mathematics, but discuss little of how to program the algorithms. This book aims to be a bridge from algorithms presented in matrix form to an actual functioning program. With that in mind, please note that this book is heavy on code and light on mathematics.

Audience

What is all this machine learning stuff and who needs it? In a nutshell, machine learning is making sense of data. So if you have data you want to understand, this book is for you. If you want to get data and make sense of it, then this book is for you too. It helps if you are familiar with a few basic programming concepts, such asrecursion and a few data structures, such as trees. It will also help if you have had an introduction to linear algebra and probability, although expertise in these fields is not necessary to benefit from this book. Lastly, the book uses Python, which has

been called “executable pseudo code” in the past. It is assumed that you have a basic working knowledge of Python, but do not worry if you are not an expert in Python—it is not difficult to learn.

Top 10 algorithms in data mining

Data and making data-based decisions are so important that even the content of this book was born out of data—from a paper which was presented at the IEEE Interna tional Conference on Data Mining titled, “Top 10 Algorithms in Data Mining” and appeared in the Journal of Knowledge and Information Systems in December, 2007. This paper was the result of the award winners from the KDD conference being asked to come up with the top 10 machine learning algorithms. The general outline of this book follows the algorithms identified in the paper. The astute reader will notice this book has 15 chapters, although there were 10 “important” algorithms. I will explain,but let’s first look at the top 10 algorithms.

The algorithms listed in that paper are: C4.5 (trees), k-means, support vector machines, Apriori, Expectation Maximization, PageRank, AdaBoost, k-Nearest Neighbors, Naïve Bayes, and CART. Eight of these ten algorithms appear in this book, the notable exceptions being PageRank and Expectation Maximization. PageRank, the algorithm that launched the search engine giant Google, is not included because I felt that it has been explained and examined in many books. There are entire books dedicated to PageRank. Expectation Maximization (EM) was meant to be in the book but sadly it is not. The main problem with EM is that it’s very heavy on the math, and when I reduced it to the simplified version, like the other algorithms in this book, I felt that there was not enough material to warrant a full chapter.

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