Andreas C. Müller & Sarah Guido
Introduction to
Machine
Learning
with P y t hon
A GUIDE FOR DATA SCIENTISTS
Andreas C. Müller and Sarah Guido
Introduction to Machine Learning
with Python
A Guide for Data Scientists
Boston Farnham Sebastopol
Tokyo
Beijing Boston Farnham Sebastopol
Tokyo
Beijing
978-1-449-36941-5
[LSI]
Introduction to Machine Learning with Python
by Andreas C. Müller and Sarah Guido
Copyright © 2017 Sarah Guido, Andreas Müller. All rights reserved.
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October 2016: First Edition
Revision History for the First Edition
2016-09-22: First Release
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Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1.
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Why Machine Learning? 1
Problems Machine Learning Can Solve 2
Knowing Your Task and Knowing Your Data 4
Why Python? 5
scikit-learn 5
Installing scikit-learn 6
Essential Libraries and Tools 7
Jupyter Notebook 7
NumPy 7
SciPy 8
matplotlib 9
pandas 10
mglearn 11
Python 2 Versus Python 3 12
Versions Used in this Book 12
A First Application: Classifying Iris Species 13
Meet the Data 14
Measuring Success: Training and Testing Data 17
First Things First: Look at Your Data 19
Building Your First Model: k-Nearest Neighbors 20
Making Predictions 22
Evaluating the Model 22
Summary and Outlook 23
iii
