# 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.

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 as recursion 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.