Himanshu Singh - Statistics for Machine Learning: Implement Statistical methods used in Machine Learning using Python
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A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem.Key FeaturesDevelop a Conceptual and Mathematical understanding of Statistics Get an overview of Statistical Applications in Python Learn how to perform Hypothesis testing in Statistics Understand why Statistics is important in Machine Learning Learn how to process data in Python DescriptionThis book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc. You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning.What you will learnUnderstand the basics of Statistics Get to know more about Descriptive Statistics Understand and learn advanced Statistics techniques Learn how to apply Statistical concepts in Python Understand important Python packages for Statistics and Machine Learning Who this book is forThis book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite.Table of Contents 1. Introduction to Statistics2. Descriptive Statistics3. Probability4. Random Variables5. Parameter Estimations6. Hypothesis Testing7. Analysis of Variance8. Regression9. Non Parametric Statistics10. Data Analysis using Python11. Introduction to Machine LearningAbout the AuthorsHimanshu Singh is an AI Technology Lead at Legato Healthcare (An Anthem Inc. Company). He has around 7 years of experience in the domain of Machine Learning and Artificial Intelligence. Himanshu is an author of three books in Machine Learning and is a trainer by passion. He is a guest faculty at various institutes like Narsee Monjee Institute of Management Studies, IMT, Vignana Jyothi Institute of Management. LinkedIn Profile: https://www.linkedin.com/in/himanshu-singh-2264a350/ Blog links: https://medium.com/@himanshuit3036Facebook Profile: https://www.facebook.com/silli23
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Statistics for
Machine Learning
Implement Statistical Methods used
in Machine Learning using Python
Himanshu Singh
www.bpbonline.com
FIRST EDITION 2021
Copyright BPB Publications, India
ISBN: 978-93-88511-97-1
All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means.
LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY
The information contained in this book is true to correct and the best of authors and publishers knowledge. The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book.
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Dedicated to
My Dad
Who never stopped believing in me,
even though he never expressed.
About the Author
Himanshu Singh is currently an AI technology lead and senior NLP developer at Legato Health Technologies (An Anthem Company). Himanshu has a total of years of experience, mostly in the domain of Natural Language Processing. He has written five books in the machine learning domain and is a guest faculty for machine learning and data science. Himanshu is an avid blogger and loves to read and write fiction short stories in his free time.
About the Reviewer
Aravind Kota is currently working as a data scientist. He has around 3+ years of experience in the field of data science, with specialization in image and text analytics and statistical operations with Python coding. He shares his knowledge in this field through blogs, and its important for readers to understand these concepts for further experiments.
Acknowledgements
First and foremost, I would like to thank my team. It is because of them that I got the opportunities to explore different problem statements, which has enabled me to write this book. I would especially like to thank Aravind, Bhavani, and Yunis sir.
I would also like to thank my students. Because of them, I came across all the doubts that they faced while understanding statistics. This, in turn, gave me ideas to approach this book in such a way that it clears the doubts of its readers.
Last but not least, I would like to thank my wife, Shikha. She has been a constant source of motivation for me, and without her, I would have never been able to finish the book.
Preface
This book can be considered a preliminary requirement before starting the machine learning journey in detail. One must understand that machine learning, in itself, is dependent on the concepts of statistics and mathematics. Statistical concepts are used in various areas of machine learning, like data exploration, finding the efficiency and efficacy of variables as well as models, and making visualizations. This book is designed in such a way that a reader can go through all the required concepts of statistics and then jump to understanding machine learning algorithms.
This book can be said to be having three sections. The first section starts with the basics of statistics. It covers preliminary concepts like mean, median, mode, and such and moves on to the concepts related to probability, random variables, and the like. The second section covers the complex parts of statistics, including advanced concepts like statistical tests, parametric and non-parametric tests and their applications in Python. Finally, the last section talks more about how to use various data science packages in Python and introduces readers to machine learning and some of its algorithms.
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Errata
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