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Hands-On Machine Learning with Scikit-Learn and TensorFlow
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Aurélien Géron
Hands-On
Machine Learning
with Scikit-Learn
& TensorFlow
CONCEPTS, TOOLS, AND TECHNIQUES
TO BUILD INTELLIGENT SYSTEMS


Aurélien Géron
Hands-On Machine Learning with
Scikit-Learn and TensorFlow
Concepts, Tools, and Techniques to
Build Intelligent Systems
Boston Farnham Sebastopol
Tokyo
Beijing Boston Farnham Sebastopol
Tokyo
Beijing

978-1-491-96229-9
[LSI]
Hands-On Machine Learning with Scikit-Learn and TensorFlow
by Aurélien Géron
Copyright © 2017 Aurélien Géron. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are
also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/insti‐
tutional sales department: 800-998-9938 or corporate@oreilly.com.
Editor: Nicole Tache
Production Editor: Nicholas Adams
Copyeditor: Rachel Monaghan
Proofreader: Charles Roumeliotis
Indexer: Wendy Catalano
Interior Designer: David Futato
Cover Designer: Randy Comer
Illustrator: Rebecca Demarest
March 2017: First Edition
Revision History for the First Edition
2017-03-10: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491962299 for release details.
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Hands-On Machine Learning with
Scikit-Learn and TensorFlow, the cover image, and related trade dress are trademarks of O’Reilly Media,
Inc.
While the publisher and the author have used good faith efforts to ensure that the information and
instructions contained in this work are accurate, the publisher and the author disclaim all responsibility
for errors or omissions, including without limitation responsibility for damages resulting from the use of
or reliance on this work. Use of the information and instructions contained in this work is at your own
risk. If any code samples or other technology this work contains or describes is subject to open source
licenses or the intellectual property rights of others, it is your responsibility to ensure that your use
thereof complies with such licenses and/or rights.

Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Part I. The Fundamentals of Machine Learning
1.
The Machine Learning Landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
What Is Machine Learning? 4
Why Use Machine Learning? 4
Types of Machine Learning Systems 7
Supervised/Unsupervised Learning 8
Batch and Online Learning 14
Instance-Based Versus Model-Based Learning 17
Main Challenges of Machine Learning 22
Insufficient Quantity of Training Data 22
Nonrepresentative Training Data 24
Poor-Quality Data 25
Irrelevant Features 25
Overfitting the Training Data 26
Underfitting the Training Data 28
Stepping Back 28
Testing and Validating 29
Exercises 31
2.
End-to-End Machine Learning Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Working with Real Data 33
Look at the Big Picture 35
Frame the Problem 35
Select a Performance Measure 37
iii
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