Who Is This Book For?

Introductory machine learning books usually focus on the what and how of machine learning (ML). They then explain the mathematical aspects of new methods from AI research labs and teach how to use AI frameworks to implement these methods. This book, on the other hand, brings together hard-earned experience around the “why” that underlies the tips and tricks that experienced ML practitioners employ when applying machine learning to real-world problems.

We assume that you have prior knowledge of machine learning and data processing. This is not a fundamental textbook on machine learning. Instead, this book is for you if you are a data scientist, data engineer, or ML engineer who is looking for a second book on practical machine learning. If you already know the basics, this book will introduce you to a catalog of ideas, some of which you (an ML practitioner) may recognize, and give those ideas a name so that you can confidently reach for them.

If you are a computer science student headed for a job in industry, this book will round out your knowledge and prepare you for the professional world. It will help you learn how to build high-quality ML systems.

What’s Not in the Book

This is a book that is primarily for ML engineers in the enterprise, not ML scientists in academia or industry research labs.

We purposefully do not discuss areas of active research—you will find very little here, for example, on machine learning model architecture (bidirectional ...