Python Machine Learning

307 Pages·2019·8.73 MB·English

by  Wei-Meng Lee

Python® Machine Learning
Python® Machine
Learning
Wei-Meng Lee
Python® Machine Learning
Published by
John Wiley & Sons, Inc.
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Copyright © 2019 by John Wiley & Sons, Inc., Indianapolis, Indiana
Published simultaneously in Canada
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ISBN: 978-1-119-54567-5 (ebk)
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Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.
I dedicate this book with love to my dearest wife (Sze Wa) and girl (Chloe),
who have to endure my irregular work schedule and for their companionship
when I am trying to meet writing deadlines!
About the Author
Wei-Meng Lee is a technologist and founder of Developer Learning Solutions
(http://www.learn2develop.net), a company specializing in hands-on training
on the latest technologies.
Wei-Meng has many years of training experience, and his training courses place
special emphasis on the learning-by-doing approach. His hands-on approach
to learning programming makes understanding the subject much easier than
just reading books, tutorials, and documentations.
Wei-Meng’s name regularly appears in online and print publications such
as DevX.com, MobiForge.com, and CoDe Magazine. You can contact Wei-Meng
at: [e-mail].
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About the Technical Editor
Doug Mahugh is a software developer who began his career in 1978 as a For-
tran programmer for Boeing. Doug has worked for Microsoft since 2005 in
a variety of roles including developer advocacy, standards engagement, and
content development. Since learning Python in 2008, Doug has written samples
and tutorials on topics ranging from caching and continuous integration to
Azure Active Directory authentication and Microsoft Graph. Doug has spoken
at industry events in over 20 countries, and he has been Microsoft’s technical
representative to standards bodies including ISO/IEC, Ecma International,
OASIS, CalConnect, and others.
Doug currently lives in Seattle with his wife Megan and two Samoyeds
named Jamie and Alice.
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Credits
Acquisitions Editor Copy Editor
Devon Lewis Kim Cofer
Associate Publisher Proofreader
Jim Minatel Nancy Bell
Editorial Manager Indexer
Pete Gaughan Potomac Indexing, LLC
Production Manager Cover Designer
Katie Wisor Wiley
Project Editor Cover Image
Gary Schwartz ©Lidiia Moor/iStockphoto-
background texture
Production Editor
© Rick_Jo/iStockphoto-digital
Barath Kumar Rajasekaran
robotic brain
Technical Editor
Doug Mahugh
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Acknowledgments
Writing a book is always exciting, but along with it come long hours of hard
work, straining to get things done accurately and correctly. To make a book
possible, a lot of unsung heroes work tirelessly behind the scenes. For this, I
would like to take this opportunity to thank a number of special people who
made this book possible.
First, I want to thank my acquisitions editor Devon Lewis, who was my first
point of contact for this book. Thank you, Devon, for giving me this opportu-
nity and for your trust in me!
Next, a huge thanks to Gary Schwartz, my project editor, who was always a
pleasure to work with. Gary is always contactable, even when he is at the air-
port! Gary has been very patient with me, even though I have missed several
of my deadlines for the book. I know it threw a spanner into his plan, but he is
always accommodating. Working with him, I know my book is in good hands.
Thank you very much, Gary!
Equally important is my technical editor—Doug Mahugh. Doug has been
very eager-eyed editing and testing my code, and never fails to let me know
if things do not work the way I intended. Thanks for catching my errors and
making the book a better read, Doug! I would also like to take this opportunity
to thank my production editor—Barath Kumar Rajasekaran. Without his hard
work, this book would not be even possible. Thanks, Barath!
Last, but not least, I want to thank my parents and my wife, Sze Wa, for all
the support they have given me. They have selflessly adjusted their schedules to
accommodate my busy schedule when I was working on this book. I love you all!
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Contents at a glance
Introduction xxiii
Chapter 1 Introduction to Machine Learning 1
Chapter 2 Extending Python Using NumPy 19
Chapter 3 Manipulating Tabular Data Using Pandas 39
Chapter 4 Data Visualization Using matplotlib 67
Chapter 5 Getting Started with Scikit-learn for Machine Learning 93
Chapter 6 Supervised Learning—Linear Regression 119
Chapter 7 S upervised Learning—Classification Using
Logistic Regression 151
Chapter 8 Supervised Learning—Classification Using Support
Vector Machines 177
Chapter 9 Supervised Learning—Classification Using K-Nearest
Neighbors (KNN) 205
Chapter 10 Unsupervised Learning—Clustering Using K-Means 221
Chapter 11 Using Azure Machine Learning Studio 243
Chapter 12 Deploying Machine Learning Models 269
Index 285
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