Preface

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields, such as search engines, robotics, self-driving cars, and so on. In this book, you will explore various real-life scenarios where you can use machine learning. You will understand what algorithms you should use in a given context using this exciting recipe-based guide.

This book starts by talking about various realms in machine learning followed by practical examples. We then move on to discuss more complex algorithms, such as Support Vector Machines, Extremely Random Forests, Hidden Markov Models, Conditional Random Fields, Deep Neural Networks, and so on. This book is for Python programmers looking to use machine learning algorithms to create real-world applications. This book is friendly to Python beginners but familiarity with Python programming will certainly be helpful to play around with the code. It is also useful to experienced Python programmers who are looking to implement machine learning techniques.

You will learn how to make informed decisions about the types of algorithm that you need to use and how to implement these algorithms to get the best possible results. If you get stuck while making sense of images, text, speech, or some other form of data, this guide on applying machine learning techniques to each of these will definitely come to your rescue!