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Bonaccorso - Machine Learning Algorithms

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An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, youll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. I ...

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Machine Learning Algorithms Second Edition Popular algorithms for data - photo 1
Machine Learning Algorithms
Second Edition
Popular algorithms for data science and machine learning
Giuseppe Bonaccorso

BIRMINGHAM - MUMBAI Machine Learning AlgorithmsSecond Edition Copyright 2018 - photo 2

BIRMINGHAM - MUMBAI
Machine Learning AlgorithmsSecond Edition

Copyright 2018 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.

Commissioning Editor: Pravin Dhandre
Acquisition Editor: Divya Poojari
Content Development Editor: Eisha Dsouza
Technical Editor: Jovita Alva
Copy Editor: Safis Editing
Project Coordinator: Namrata Swetta
Proofreader: Safis Editing
Indexer: Tejal Daruwale Soni
Graphics: Jisha Chirayil
Production Coordinator: Nilesh Mohite

First published: July 2017
Second edition: August 2018

Production reference: 1280818

Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.

ISBN 978-1-78934-799-9

www.packtpub.com


To my family and to all the people who always believed in me and encouraged me in this long journey!

Giuseppe Bonaccorso
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Contributors
About the author

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

I want to thank the people who have been close to me and have supported me, especially my parents, who never stopped encouraging me.
About the reviewer

Doug Ortiz is an experienced enterprise cloud, big data, data analytics, and solutions architect who has architected, designed, developed, re-engineered, and integrated enterprise solutions. Other expertise includes Amazon Web Services, Azure, Google Cloud, business intelligence, Hadoop, Spark, NoSQL databases, and SharePoint, to name a few.

He is the founder of Illustris, LLC and is reachable at dougortiz@illustris.org.

Huge thanks to my wonderful wife, Milla, Maria, Nikolay, and our children for all their support.
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Preface

This book is an introduction to the world of machine learning, a topic that is becoming more and more important, not only for IT professionals and analysts but also for all the data scientists and engineers who want to exploit the enormous power of techniques such as predictive analysis, classification, clustering, and natural language processing. In order to facilitate the learning process, all theoretical elements are followed by concrete examples based on Python.

A basic but solid understanding of this topic requires a foundation in mathematics, which is not only necessary to explain the algorithms, but also to let the reader understand how it's possible to tune up the hyperparameters in order to attain the best possible accuracy. Of course, it's impossible to cover all the details with the appropriate precision. For this reason, some topics are only briefly described, limiting the theory to the results without providing any of the workings. In this way, the user has the double opportunity to focus on the fundamental concepts (without too many mathematical complications) and, through the references, examine in depth all the elements that generate interest.

The chapters can be read in no particular order, skipping the topics that you already know. Whenever necessary, there are references to the chapters where some concepts are explained. I apologize in advance for any imprecision, typos or mistakes, and I'd like to thank all the Packt editors for their collaboration and constant attention.

Who this book is for

This book is for machine learning engineers, data engineers, and data scientists who want to build a strong foundation in the field of predictive analytics and machine learning. Familiarity with Python would be an added advantage and will enable you to get the most out of this book.

What this book covers

, A Gentle Introduction to Machine Learning, introduces the world of machine learning, explaining the fundamental concepts of the most important approaches to creating intelligent applications and focusing on the different kinds of learning methods.

, Important Elements in Machine Learning

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