Audience

Who do I think you are? I’ve written Machine Learning with Python for Everyone for the absolute beginner to machine learning. Even more so, you may well have very little college-level mathematics in your toolbox and I’m not going to try to change that. While many machine learning books are very heavy on mathematical concepts and equations, I’ve done my best to minimize the amount of mathematical luggage you’ll have to carry. I do expect, given the book’s title, that you’ll have some basic proficiency in Python. If you can read Python, you’ll be able to get a lot more out of our discussions. While many books on machine learning rely on mathematics, I’m relying on stories, pictures, and Python code to communicate with you. There will be the occasional equation. Largely, these can be skipped if you are so inclined. But, if I’ve done my job well, I’ll have given you enough context around the equation to maybe—just maybe—understand what it is trying to say.

Why might you have this book in your hand? The least common denominator is that all of my readers want to learn about machine learning. Now, you might be coming from very different backgrounds: a student in an introductory computing class focused on machine learning, a mid-career business analyst who all of sudden has been thrust beyond the limits of spreadsheet analysis, a tech hobbyist looking to expand her interests, or a scientist needing to analyze data in a new way. Machine learning is permeating society. Depending on your background, Machine Learning with Python for Everyone has different things to offer you. Even a mathematically sophisticated reader who is looking to do a break-in to machine learning using Python can get a lot out of this book.

So, my goal is to take someone with an interest or need to do some machine learning and teach them the process and the most important concepts of machine learning in a concrete way using the Python scikit-learn library and some of its friends. You’ll come away with overall patterns, strategies, pitfalls, and gotchas that will be applicable in every learning system you ever study, build, or use.

Approach

Many books that try to explain mathematical topics, such as machine learning, do so by presenting equations as if they tell a story to the uninitiated. I think that leaves many of us—even those of us who like mathematics!—stuck. Personally, I build a far better mental picture of the process of machine learning by combining visual and verbal descriptions with running code. I’m a computer scientist at heart and by training. I love building things. Building things is how I know that I’ve reached a level where I really understand them. You might be familiar with the phrase, “If you really want to know something, teach it to someone.” Well, there’s a follow-on. “If you really want to know something, teach a computer to do it!” That’s my take on how I’m going to teach you machine learning. With minimal mathematics, I want to give you the concepts behind the most important and frequently used machine learning tools and techniques. Then, I want you to immediately see how to make a computer do it. One note: we won’t be programming these methods from scratch. We’ll be standing on the shoulders of giants and using some very powerful, time-saving, prebuilt software libraries (more on that shortly).

We won’t be covering all of these libraries in great detail—there is simply too much material to do that. Instead, we are going to be practical. We are going to use the best tool for the job. I’ll explain enough to orient you in the concept we’re using—and then we’ll get to using it. For our mathematically inclined colleagues, I’ll give pointers to more in-depth references they can pursue. I’ll save most of this for end-of-the-chapter notes so the rest of us can skip it easily.

If you are flipping through this introduction, deciding if you want to invest time in this book, I want to give you some insight into things that are out-of-scope for us. We aren’t going to dive into mathematical proofs or rely on mathematics to explain things. There are many books out there that follow that path and I’ll give pointers to my favorites at the ends of the chapters. Likewise, I’m going to assume that you are fluent in basic- to intermediate-level Python programming. However, for more advanced Python topics—and things that show up from third-party packages like NumPy or Pandas—I’ll explain enough of what’s going on so that you can understand each technique and its context.

Overview

In Part I, we establish a foundation. I’ll give you some verbal and conceptual introductions to machine learning in Chapter 1. In Chapter 2 we introduce and take a slightly different approach to some mathematical and computational topics that show up repeatedly in machine learning. Chapters 3 and 4 walk you through your first steps in building, training, and evaluating learning systems that classify examples (classifiers) and quantify examples (regressors).

Part II shifts our focus to the most important aspect of applied machine learning systems: evaluating the success of our system in a realistic way. Chapter 5 talks about general evaluation techniques that will apply to all of our learning systems. Chapters 6 and 7 take those general techniques and add evaluation capabilities for classifiers and regressors.

Part III broadens our toolbox of learning techniques and fills out the components of a practical learning system. Chapters 8 and 9 give us additional classification and regression techniques. Chapter 10 describes feature engineering: how we smooth the edges of rough data into forms that we can use for learning. Chapter 11 shows how to chain multiple steps together as a single learner and how to tune a learner’s inner workings for better performance.

Part IV takes us beyond the basics and discusses more recent techniques that are driving machine learning forward. We look at learners that are made up of multiple little learners in Chapter 12. Chapter 13 discusses learning techniques that incorporate automated feature engineering. Chapter 14 is a wonderful capstone because it takes the techniques we describe throughout the book and applies them to two particularly interesting types of data: images and text. Chapter 15 both reviews many of the techniques we discuss and shows how they relate to more advanced learning architectures—neural networks and graphical models.

Our main focus is on the techniques of machine learning. We will investigate a number of learning algorithms and other processing methods along the way. However, completeness is not our goal. We’ll discuss the most common techniques and only glance briefly at the two large subareas of machine learning: graphical models and neural, or deep, networks. However, we will see how the techniques we focus on relate to these more advanced methods.

Another topic we won’t cover is implementing specific learning algorithms. We’ll build on top of the algorithms that are already available in scikit-learn and friends; we’ll create larger solutions using them as components. Still, someone has to implement the gears and cogs inside the black-box we funnel data into. If you are really interested in implementation aspects, you are in good company: I love them! Have all your friends buy a copy of this book, so I can argue I need to write a follow-up that dives into these lower-level details.

Acknowledgments

I must take a few moments to thank several people that have contributed greatly to this book. My editor at Pearson, Debra Williams Cauley, has been instrumental in every phase of this book’s development. From our initial meetings, to her probing for a topic that might meet both our needs, to gently shepherding me through many (many!) early drafts, to constantly giving me just enough of a push to keep going, and finally climbing the steepest parts of the mountain at its peak . . . through all of these phases, Debra has shown the highest degrees of professionalism. I can only respond with a heartfelt thank you.

My wife, Dr. Barbara Fenner, also deserves more praise and thanks than I can give her in this short space. In addition to the burdens that any partner of an author must bear, she also served as my primary draft reader and our intrepid illustrator. She did the hard work of drafting all of the non-computer-generated diagrams in this book. While this is not our first joint academic project, it has been turned into the longest. Her patience is, by all appearances, never ending. Barbara, I thank you!

My primary technical reader was Marilyn Roth. Marilyn was unfailingly positive towards even my most egregious errors. Machine Learning with Python for Everyone is immeasurably better for her input. Thank you.

I would also like to thank several members of Pearson’s editorial staff: Alina Kirsanova and Dmitry Kirsanov, Julie Nahil, and many other behind-the-scenes folks that I didn’t have the pleasure of meeting. This book would not exist without you and your hardworking professionalism. Thank you.

Publisher’s Note

The text contains unavoidable references to color in figures. To assist readers of the print edition, color PDFs of figures are available for download at http://informit.com/title/9780134845623.

For formatting purposes, decimal values in many tables have been manually rounded to two place values. In several instances, Python code and comments have been slightly modified—all such modifications should result in valid programs.

Online resources for this book are available at https://github.com/mfenner1.