Giuseppe Bonaccorso - Mastering Machine Learning Algorithms - Second Edition
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Mastering Machine Learning Algorithms
Second Edition
Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work
Giuseppe Bonaccorso
BIRMINGHAM - MUMBAI
Mastering Machine Learning Algorithms
Second Edition
Copyright 2020 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Acquisition Editor: Tushar Gupta
Acquisition Editor Peer Reviews: Suresh Jain
Content Development Editor: Alex Patterson
Technical Editor: Gaurav Gavas
Project Editor: Kishor Rit
Proofreader: Safis Editing
Indexer: Rekha Nair
Production Designer: Sandip Tadge
First published: May 2018
Second edition: January 2020
Production reference: 1300120
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-83882-029-9
www.packt.com
packt.com
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Contributors
Giuseppe Bonaccorso is an experienced data science manager with expertise in machine/deep learning. He got his M.Sc. Eng. in Electronics Engineering in 2005 from the University of Catania, Italy and continued his studies (MBA) at the University of Rome "Tor Vergata," Italy and the University of Essex, UK. His main interests include machine/deep learning, data science strategy, and digital innovation in the healthcare industry.
Luca Massaron is a data scientist with more than a decade of experience in transforming data into smarter artifacts, in solving real-world problems and in generating value for businesses and stakeholders. He is also the author of best-selling books on AI, machine learning, and algorithms; a Kaggle master who reached no 7 in the worldwide user rankings for his performance in data science competitions; and a Google Developer Expert in machine learning.
My greatest thanks go to my family, Yukiko and Amelia, for their support and loving patience.
In the last few years, machine learning has become an increasingly important field in the majority of industries. Several processes 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 that 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 the second edition of Mastering Machine Learning Algorithms 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 (using the most advanced and common frameworks). I wanted to keep a pragmatic approach, focusing on the applications but never forgetting the theoretical foundations. A solid knowledge of this field, in fact, 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 general cases and let the reader improve and adapt them to cope with particular scenarios. I apologize for mistakes: even though many revisions have been made, it's possible that some details (both in the formulas and in the code) got away.
In particular, the second edition corrects some typos and mistakes present in the first one, improves the readability of some complex topics, and is based on the most recent version of production-ready frameworks (like TensorFlow 2.0). Given the overall complexity of the work, I apologize since despite the hard work of the author and all editors, it's always possible to find imprecisions or errors.
I've finished this book in a particular period of my life and I'd like to dedicate it to my father, an artist and art professor, who has been always a guide for me, teaching me how it's always possible to join scientific rigor with an artistic approach. At the end of the day, data science needs creativity and, conversely, creativity can find in data science an extremely fertile soil!
This book is a relevant source of content (both theoretical and practical) for data science professionals and machine learning engineers who want to deep dive into the complex machine learning algorithms, the calibration of the models, and to improve the predictions of the trained model. A solid knowledge of basic machine learning is required to get the best out of this mastery guide. Moreover, given the complexity of some topics, a good mathematical background is necessary.
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