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本书通过数学解释和编程实例提供了机器学习的概念。每章从真实世界数据集的技术基础和工作示例开始。除了应用算法的建议之外，每种技术都具有数据上的优点和缺点。在本书中，我们提供了python中的代码示例。 Python是最适合和全世界接受的语言。首先，它是免费且开源的。它包含来自开放社区的非常好的支持。它包含很多库，所以你不需要编码所有的东西。此外，它可以扩展大量数据并适用于大数据技术。 本书：涵盖机器学习的所有主要领域。主题将通过图形解释进行讨论。不同机器学习方法比较解决任何问题。在应用任何机器学习算法之前处理真实世界噪声数据的方法。讨论每个概念的Python代码示例。 Jupyter笔记本脚本提供用于测试和尝试算法的数据集目录机器学习简介了解Python特征工程数据可视化基本和高级回归技术分类无监督学习文本分析神经网络和深度学习推荐系统时间序列分析。
An Approach to Applied Machine Learning
• I � � •
Fundamentals of the Techni
Machine Learning with Python i
An Approach to Applied Machine Learning
FIRST EDITION 2018
Copyright © BPB Publications, INDIA
All Rights Reserved. No part of this publication can be stored in a retrieval system or
reproduced in any form or by any means without the prior written permission of the
LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY
The Author and Publisher of this book have tried their best to ensure that the programmes,
procedures and functions described in the book are correct. However, the author and the
publishers make no warranty of any kind, expressed or implied, with regard to these
programmes or the documentation contained in the book. The author and publisher shall
not be liable in any event of any damages, incidental or consequential, in connection
with, or arising out of the furnishing, performance or use of these programmes, procedures
and functions. Product name mentioned are used for identification purposes only and
may be trademarks of their respective companies.
All trademarks referred to in the book are acknowledged as properties of their respective
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Published by Manish Jain for BPB Publications, 20, Ansari Road, Darya Ganj, New
Delhi-110002 and Printed by Repro India Ltd., Mumbai
To my father, S.N. Vijay
my mother, Meena Vijay
and my wife, Ankita Jain
This book is dedicated to my dearest parents and beloved wife who
ever and ever supported me in all respects of life and career.
“Family is not an important thing. It’s everything.”
-Michael J. Fox
Machine Learning is the next big thing in computer science. From manufacturing
to e-Commerce and shipping, it is used everywhere and provides solution which
are based on the data. The idea behind Machine Learning is to make a
knowledgeable Model which is as intelligent as human in taking a decision. Now
with the increasing power of computation and memory, Machine Learning can
provide even better solution than humans.
This book is organized in 12 chapters. Each chapter touches one of the primary
area of machine learning.
First chapter of this book starts with basic Introduction of Machine Learning.
Reader can get the idea of general Machine L earni n g Algorithms with their use
to solve different types of real world problem. Chapter 2 contains basics of Python
Language. Python is an open source language and a very good tool for
applying Machine Learning. That ’s why we have chosen it to program Machine
Learning Algorithms. Chapter 3 provides techniques to perform Featu re
Engineering. Finding correct features and modifying th em is equally
important in Machine Learning as Algorithms. Chapter 4 focused on Data
VisualizaJon Techniques. With use of pre- built Python Library, user can visualize
the data and present it to others.
Although the content and codes are checked by other Data Scientists, there may
be some shortcomings. Author welcomes your suggestions and criticism i f any.
Author will try his best to remove those errors in future editions of this book.
Chapter 9 to 12 provides knowledge on advanced concepts in Machine Learning.
Chapter 9 covers Text Analysis. It contains an example to classify news in predefined
category. Neural Networks a n d Deep Learning is discussed in chapter 10. This is
highly used in unstructured, image and voice data. Chapter 11 p rovides methods
of building a Recommendation System with examples in each category. Last chapter
12 discusses Time Series Data, methods to handle and make forecasting on it.
This book is helpful for all types of readers. Either you want to start in machine
learning or want to learn the concepts more or pracIce with the co d e, i t provides
everything. We recommend users to learn the concept and practice it by using
sample code to get the fu l l of this book.
Chapter 5 to 7 contains Supervised Learning Algorithms. Chapter 5 ex p l ains Basic
Regression Techniques with example on a real-world data. Chapter 6 fo cu ses on
Advanced Regression Techniques with the solution of overﬁtCng problem. Chapter
7 provides details of classificaCon Algorithms. It contains both parameterized and
non-parameterized techni ques to solve the problem. Chapter 8 gives idea of
Unsu p ervised Learning, mai n ly Cl u stering.
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