One could argue that it is a fortunate coincidence that you are holding this book in your hands (or have it on your eBook reader). After all, there are millions of books printed every year, which are read by millions of readers. And then there is this book read by you. One could also argue that a couple of machine learning algorithms played their role in leading you to this book—or this book to you. And we, the authors, are happy that you want to understand more about the hows and whys. Most of the book will cover the how. How has data to be processed so that machine learning algorithms can make the most out of it? How should one choose the right algorithm for a problem at hand? Occasionally, we will also cover the why. Why is it important to measure correctly? Why does one algorithm outperform another one in a given scenario? We know that there is much more to learn to be an expert in the field. After all, we only covered some hows and just a tiny fraction of the whys. But in the end, we hope that this mixture will help you to get up and running as quickly as possible.