BONACCORSO - MASTERING MACHINE LEARNING ALGORITHMS.

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First published: May 2018

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ISBN 978-1-78862-111-3

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Giuseppe Bonaccorso is an experienced team leader/manager in AI, machine/deep learning solution design, management, and delivery. He got his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, cryptocurrencies, and NLP.

FrancescoAzzola is an electronics engineer with over 15 years of experience in computer programming. He is the author of Android Things Projects by Packt. He loves creating IoT projects using Arduino, Raspberry Pi, Android, and other IoT platforms. He is interested in convergence of IoT and mobile applications. He is c ertified in SCEA, SCWCD, and SCJP.

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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!

The ideal audience for this book is computer science students and professionals who want to acquire detailed knowledge of complex machine learning algorithms and applications. The approach is always pragmatic; however, the theoretical part requires some advanced mathematical skills that all graduates (in computer science, engineering, mathematics, or science) should have acquired. The book can be also utilized by more business-oriented professionals (such as CPOs and product managers) to understand how machine learning can be employed to improve existing products and businesses.