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Mastering Machine Learning with Python Programming Language

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Machine Learning is making the computer learn from studying data and statistics. Machine Learning is a step into the direction of artificial intelligence (AI). Machine Learning is a program that analyses data and learns to predict the outcome.

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Mastering Machine
Learning with
Python in Six Steps
A Practical Implementation Guide to
Predictive Data Analytics Using Python

Manohar Swamynathan

,Mastering Machine
Learning with
Python in Six Steps
A Practical Implementation Guide
to Predictive Data Analytics Using
Python




Manohar Swamynathan

,Mastering Machine Learning with Python in Six Steps
Manohar Swamynathan
Bangalore, Karnataka, India
ISBN-13 (pbk): 978-1-4842-2865-4 ISBN-13 (electronic): 978-1-4842-2866-1
DOI 10.1007/978-1-4842-2866-1
Library of Congress Control Number: 2017943522
Copyright © 2017 by Manohar Swamynathan
This work is subject to copyright. All rights are reserved by the Publisher, whether the
whole or part of the material is concerned, specifically the rights of translation, reprinting,
reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any
other physical way, and transmission or information storage and retrieval, electronic
adaptation, computer software, or by similar or dissimilar methodology now known or
hereafter developed.
Trademarked names, logos, and images may appear in this book. Rather than use a
trademark symbol with every occurrence of a trademarked name, logo, or image we
use the names, logos, and images only in an editorial fashion and to the benefit of the
trademark owner, with no intention of infringement of the trademark.
The use in this publication of trade names, trademarks, service marks, and similar terms,
even if they are not identified as such, is not to be taken as an expression of opinion as to
whether or not they are subject to proprietary rights.
While the advice and information in this book are believed to be true and accurate at the
date of publication, neither the authors nor the editors nor the publisher can accept any
legal responsibility for any errors or omissions that may be made. The publisher makes
no warranty, express or implied, with respect to the material contained herein.
Cover image designed by Freepik
Managing Director: Welmoed Spahr
Editorial Director: Todd Green
Acquisitions Editor: Celestin Suresh John
Development Editor: Anila Vincent and James Markham
Technical Reviewer: Jojo Moolayil
Coordinating Editor: Sanchita Mandal
Copy Editor: Karen Jameson
Compositor: SPi Global
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Artist: SPi Global
Distributed to the book trade worldwide by Springer Science+Business Media New York,
233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201)
348-4505, e-mail , or visit www.springeronline.com.
Apress Media, LLC is a California LLC and the sole member (owner) is Springer
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Print and eBook Bulk Sales web page at http://www.apress.com/bulk-sales.
Any source code or other supplementary material referenced by the author in this book is
available to readers on GitHub via the book’s product page, located at www.apress.com/
978-1-4842-2865-4. For more detailed information, please visit http://www.apress.com/
source-code.
Printed on acid-free paper

, Contents at a Glance

About the Author���������������������������������������������������������������������������� xiii
About the Technical Reviewer��������������������������������������������������������� xv
Acknowledgments������������������������������������������������������������������������� xvii
Introduction������������������������������������������������������������������������������������ xix


■Chapter 1: Step 1 – Getting Started in Python�������������������������������� 1

■Chapter 2: Step 2 – Introduction to Machine Learning����������������� 53

■Chapter 3: Step 3 – Fundamentals of Machine Learning������������ 117

■Chapter 4: Step 4 – Model Diagnosis and Tuning����������������������� 209

■Chapter 5: Step 5 – Text Mining and Recommender Systems���� 251

■Chapter 6: Step 6 – Deep and Reinforcement Learning�������������� 297

■Chapter 7: Conclusion���������������������������������������������������������������� 345

Index���������������������������������������������������������������������������������������������� 351




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