In the last few years, machine learning has become a more and more important field in the majority of industries. Many tasks once considered impossible to automate are now completely managed by computers, allowing human beings to focus on more creative tasks. This revolution has been made possible by the dramatic improvement of standard algorithms, together with a continuous reduction in hardware prices. The complexity that was a huge obstacle only a decade ago is now a problem than even a personal computer can solve. The general availability of high-level open source frameworks has allowed everybody to design and train extremely powerful models.
The main goal of this book is to introduce the reader to complex techniques (such as semi-supervised and manifold learning, probabilistic models, and neural networks), balancing mathematical theory with practical examples written in Python. I wanted to keep a pragmatic approach, focusing on the applications but not neglecting the necessary theoretical foundation. In my opinion, a good knowledge of this field can be acquired only by understanding the underlying logic, which is always expressed using mathematical concepts. This extra effort is rewarded with a more solid awareness of every specific choice and helps the reader understand how to apply, modify, and improve all the algorithms in specific business contexts.
Machine learning is an extremely wide field and it's impossible to cover all the topics in a book. In this case, I've done my best to cover a selection of algorithms belonging to supervised, semi-supervised, unsupervised, and Reinforcement Learning, providing all the references necessary to further explore each of them. The examples have been designed to be easy to understand without any deep insight into the code; in fact, I believe it's more important to show the general cases and let the reader improve and adapt them to cope with particular scenarios. I apologize for mistakes: even if many revisions have been made, it's possible that some details (both in the formulas and in the code) got away. I hope this book will be the starting point for many professionals struggling to enter this fascinating world with a pragmatic and business-oriented viewpoint!
