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
C
on
cepts, Tools, and Techniques to
Build Intelligent Systems
Boston Farnham Sebastopol TokyoBeijing Boston Farnham Sebastopol TokyoBeijing
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.
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March 2017: First Edition
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2017-03-10:
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Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xiii
Part I. The Fundamentals of Machine Learning
1. The Machine Learning Landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
Wha
t 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
