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Machine Learning Master Supervised and Unsupervised Learning Algorithms with Real Examples (English Edition) (Doshi, Dr Ruchi, Hiran, Dr Kamal Kant, Jain etc.) (Z-Library)
AIAuthor：Doshi, Dr Ruchi, Hiran, Dr Kamal Kant, Jain, Ritesh Kumar, Lakhwani, Dr Kamlesh
Concepts of Machine Learning with Practical Approaches. Key Features ● Includes real-scenario examples to explain the working of Machine Learning algorithms. ● Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. ● Full of Python codes, numerous exercises, and model question papers for data science students. Description The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches. This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning. What you will learn ● Perform feature extraction and feature selection techniques. ● Learn to select the best Machine Learning algorithm for a given problem. ● Get a stronghold in using popular Python libraries like Scikit-learn, pandas, and matplotlib. ● Practice how to implement different types of Machine Learning techniques. Who this book is for This book is designed for data science and analytics students, academicians, and researchers who want to explore the concepts of machine learning and practice the understanding of real cas
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Machine Learning Master Supervised and Unsupervised Learning Algorithms with Real Examples Dr Ruchi Doshi Dr Kamal Kant Hiran Ritesh Kumar Jain Dr Kamlesh Lakhwani www.bpbonline.com
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FIRST EDITION 2022 Copyright © BPB Publications, India ISBN: 978-93-91392-352 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 author’s and publisher’s 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. All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information.
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www.bpbonline.com
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Foreword Recently, machine learning has been utilized by the governments, businesses and general public for different purposes. Machine learning has assisted organizations by generating profit, and its popularity among developers and technologists has skyrocketed. To best assist the readers in better understanding of the facts, this book is well arranged in a way that teaches them what the facts are. — Dr. Ricardo Saavedra Director & Chair International Programs Universidad Azteca, Mexico In today's digital age, mastering Machine Learning is a must. This book will elegantly guide you through everything you need to know about this topic. — Dr. Govind Kumawat Indian Institute of Management, Udaipur, India The authors provide an easy-to-understand and comprehensive overview of Machine Learning concepts. The explanation is clear and concise, with appropriate diagrams and real-world examples that help to demystify this emerging technology. — Dr. Deepak Khazanchi University of Nebraska at Omaha, USA
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This book covers a wide range of learning approaches, with machine learning techniques and algorithms with detailed examples to accompany each approach. — Dr. Samuel Agbesi Aalborg University, Denmark The adoption and prevalence of Artificial Intelligence and Machine Learning in our daily lives are the two most significant technological shifts in the 21st century. This book explains the concepts of Machine Learning technologies in a concise, clear and lucid manner. — Dr. Shiva Raj Pokhrel Deakin University, Australia A genuine book for those who want to learn and apply Machine Learning concepts. — Prof. Dr. Dharm Singh Namibia University of Science and Technology, Namibia Machine Learning is a fascinating and important research topic these days. The book also transitions from academic to research topics. As a result, it is extremely beneficial to any researcher or academician, from beginner to advanced level.
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— Trilok Nuwal Microsoft, India The book is extremely comprehensive and can be used in conjunction with any university's curriculum. The best part of the book is that it discusses machine learning algorithms with real- world examples and practical applications. — Dr. Tanima Dutta Indian Institute of Technology (BHU), India Machine Learning is a game changer in the age of digitization. This book covers almost every aspect of Machine Learning, from the fundamentals to the application level. — Abhishek Maloo Twitter, California, USA Machine Learning, like electricity, will revolutionize our lives in a variety of ways, some of which are not even imaginable today. This book offers a comprehensive conceptual understanding of Machine Learning techniques and algorithms. — Desmond Okocha, PhD Bingham University, Nigeria This book provides an in-depth introduction to Machine Learning even to readers with no pre-requisite knowledge. Many
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mathematical concepts are explained in an easy-to-understand manner. — Dr. Patrick Acheampong Ghana Communication Technology University, Ghana Provides a comprehensive overview of available techniques and algorithms in conceptual terms, encompassing a variety of machine learning application domains. — Dr. Sumarga Kumar Sah Tyagi Zhongyuan University of Technology, China This book covers everything fundamental to machine learning, to immerse yourself in the theory of the topic and to use practical applications and examples to promote knowledge. — Dr. Albert Gyamfi Saskatchewan Polytechnic, Canada In addition to covering the theoretical aspects of machine learning, the authors teach the various techniques for obtaining data as well as how to use different inputs and outputs to evaluate results. Machine learning is dynamic, so the methods are always evolving. — Do Manh Thai Govt. Executive, Vietnam
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This book includes the popular learning algorithms, techniques and implementations in the artificial intelligence field. I strongly advise this book. — Prof. Vinesh Jain Govt. Engineering College, Ajmer, India
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Dedicated to Our lovely little daughter Miss Bhuvi Jain. Your endless love and energy charge every day. – Dr. Ruchi Doshi and Dr. Kamal Kant Hiran
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About the Authors Dr. Ruchi Doshi has more than 14 years of academic, research and software development experience in Asia and Africa. Currently, she is working as a Research Supervisor at the Universidad Azteca, Mexico and Adjunct faculty at the Jyoti Vidyapeeth Women’s University, Jaipur, Rajasthan, India. She has worked in the BlueCrest University College, Liberia, West Africa as Registrar and Head, Examination, BlueCrest University College, Ghana, Africa, Amity University, Rajasthan, India and Trimax IT Infrastructure and Services, Udaipur, India. She has been nominated by the IEEE Headquarter, USA for the Chair, Women in Engineering and Secretary Position in Liberia country. She has worked with the Ministry of Higher Education (MoHE) in Liberia and Ghana for the Degree approval and accreditation processes. She is interested in the field of Machine Learning and Cloud computing framework development. She has given many expert talks in the area of Women in Research, Use of Machine Learning Technology in Real-time Applications and Community Based Participatory Action Research at the national and international level. She has published 25 scientific research papers in SCI/Scopus/Web of Science Journals, Conferences, 2 Indian Patents and 4 books with internationally renowned publishers. She is a reviewer, advisor, ambassador and editorial board member of various reputed international journals and conferences. She is an active member in organizing many
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international seminars, workshops and conferences in Mexico, India, Ghana and Liberia. LinkedIn Profile: https://www.linkedin.com/in/dr-ruchi-doshi- 96bb63b4/ Dr. Kamal Kant Hiran is an Assistant Professor, School of Engineering at the Sir Padampat Singhania University (SPSU), Udaipur, Rajasthan, India as well as a Research Fellow at the Aalborg University, Copenhagen, He has more than 16 years of experience as an academic and researcher in Asia, Africa and Europe. He has worked as an Associate Professor and Head, Academics at the BlueCrest University College, Liberia, West Africa, Head of Department at the Academic City College, Ghana, West Africa, Senior Lecturer at the Amity University, Jaipur, Rajasthan, India, Assistant Professor at the Suresh Gyan Vihar University, Jaipur, Rajasthan, India and Visiting Lecturer at the Government Engineering College, Ajmer. He has several awards to his credit such as the international travel grant for attending the 114th IEEE Region 8 Committee meeting in Warsaw, Poland, International travel grant for Germany from ITS Europe, Passau, Germany, Best Research Paper Award at the University of Gondar, Ethiopia and SKIT, Jaipur, India, IEEE Liberia Subsection Founder Award, Gold Medal Award in M. Tech (Hons.), IEEE Ghana Section Award - Technical and Professional Activity Chair, IEEE Senior Member Recognition, IEEE Student Branch Award and Elsevier Reviewer Recognition Award. He has published 35 scientific research papers in SCI/Scopus/Web of Science and IEEE Transactions Journal, Conferences, 2 Indian
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Patents, 1 Australian patent grant and 9 books with internationally renowned publishers. He is a reviewer and editorial board member of various reputed international journals in Elsevier, Springer, IEEE Transactions, IET, Bentham Science and IGI Global. He is an active member in organizing many international seminars, workshops and conferences. He has made several international visits to Denmark, Sweden, Germany, Poland, Norway, Ghana, Liberia, Ethiopia, Russia, Dubai and Jordan for research exposures. His research interests focus on Cloud Computing, Machine Learning and Intelligent IoT. LinkedIn Profile: https://www.linkedin.com/in/kamal-kant-hiran-phd- 4553b643/ Mr. Ritesh Kumar Jain works as an Assistant Professor at the Geetanjali Institute of Technical Studies, (GITS), Udaipur, Rajasthan, India. He has more than 15 years of teaching and research experience. He has completed his BE and MTech. He has worked as an Assistant Professor and Head of Department at S.S. College of Engineering. Udaipur, Assistant Professor at Sobhasaria Engineering College, Sikar and Lecturer at the Institute of Technology and Management, Bhilwara. He is a reviewer of international peer-reviewed journals. He is the author of several research papers in peer-reviewed international journals and conferences. LinkedIn Profile: https://www.linkedin.com/in/ritesh-jain-b8924345/
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Dr. Kamlesh Lakhwani works as an Associate Professor at the School of Computer Science and Engineering, JECRC University, Jaipur, India. He has an excellent academic background and a rich experience of 15 years as an academician and researcher in Asia. As a prolific writer in the arena of Computer Sciences and Engineering, he has penned down several learning materials on C, C++, Multimedia Systems, Cloud Computing, IoT, Image Processing, etc. He has four published patents to his credit and contributed for more than 50 research papers in the Conferences/Journals/Seminar of International and National repute. His area of interest includes Cloud Computing, Internet of Things, Computer vision, Image processing, Video Processing and Machine Learning. LinkedIn Profile: https://www.linkedin.com/in/dr-kamlesh-lakhwani- 7119944b/
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About the Reviewer Dr. Ajay Kumar Vyas has more than 15 years of teaching and research experience and is presently working as an Assistant Professor at Adani Institute of Infrastructure Engineering, Ahmedabad (India). He has completed his Bachelor of Engineering (2005) in Electronics and Communication from Govt. Engineering College, Ujjain and M.Tech (2009) in Optical Communication from Shri Govindram Sakseriya Institute of Tech and Sci., Indore with Honors and PhD (2016) from Maharana Pratap Agri. and Tech. University, Udaipur (Raj). He is a senior member of IEEE and IACSIT (Singapore). He has been awarded certificate of excellence from Elsevier Research Academic and Publons Academy as a certified peer reviewer. He has worked as a reviewer for renowned journals of Springer, IET, IEEE, OSA, IGI Global, Chinese Journal of Electrical Engineering and many more. He is the author of several research papers in peer-reviewed international journals and conferences, three books with De-Gruyter and India Publications and has published two Indian patents. He is also the author of many book chapters published by Springer International Publishing, Singapore.
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Acknowledgement First and foremost, we'd like to thank the Almighty for giving us the inspiration and zeal to write this book. Our sincere thanks goes to our organizations, Universidad Azteca, Mexico, Sir Padampat Singhania University, Geetanjali Institute of Technical Studies, JECRC University, India for providing us with a healthy academic and research environment during work. Special thanks to the BPB Publications team, especially to Nrip Jain and members for their support, advice and assistance in editing and publishing this book. The completion of this book could not have been possible without the contribution and support we got from our family, friends and colleagues. It is a pleasant aspect and we express our gratitude to all of them. — Dr. Ruchi Doshi Universidad Azteca, Mexico — Dr. Kamal Kant Hiran Sir Padampat Singhania University (SPSU), India — Mr. Ritesh Kumar Jain
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Geetanjali Institute of Technical Studies (GITS), India — Dr. Kamlesh Lakhwani JECRC University, India
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Preface Machine learning is an application of Artificial Intelligence (AI). While AI is the umbrella term given to machines emulating human abilities, machine learning is a specific branch of AI where machines are trained to learn how to process and make use of data. The objective of machine learning is not only for effective data collection but also to make use of the ever-increasing amounts being gathered by manipulating and analyzing them without heavy human input. Machine learning can be defined as a method of mathematical analysis, often using well-known and familiar methods, with a different focus than the traditional analytical practice in applied subjects. The key idea is that flexible and automated methods are used to find patterns within data with a primary focus on making predictions for future data. There are several real-time applications of machines such as Image Recognition, Biometric Recognition, Speech Recognition, Handwriting Recognition, Medical Diagnosis, Traffic prediction, Text Retrieval, Product recommendations, Self-driving cars, Virtual Personal Assistants, Online Fraud Detection, Natural Language Processing and so on. Machine Learning paradigms are defined in three types namely Supervised Learning, Unsupervised Learning and Reinforcement Learning. Supervised learning algorithms are designed to learn by
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example. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. Unsupervised learning deals with unlabelled data which means here we have input data and no corresponding output variable. This is further classified into Clustering and Association. In Reinforcement the machine or agent automatically learns using feedback without any labelled data. Here, the agent learns by itself from its experience. In this book, the reader will not only find the theoretical concepts but also the practical knowledge needed to quickly and efficiently apply these strategies to challenging problems of machine learning. The reader learns how to understand a problem, be able to represent data, select and correct skills, interpret results correctly and practice effective analysis of outcomes to make strategic decisions. Organization of the Book The book consists of six chapters, in which the reader will learn the following: Chapter 1 introduces the fundamental concepts of machine learning, its applications, types and describes the setup we will be using throughout the book. Chapter 2 describes supervised machine learning. Different supervised machine learning algorithms such as Linear Regression Model, Naive Bayes classifier Decision Tree, K nearest neighbor, Logistic Regression, Support Vector Machine, and Random forest algorithm are described in this chapter with their practical use.
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Chapter 3 describes unsupervised machine learning. Different unsupervised machine learning algorithms such as K-Means Clustering, Hierarchical Clustering, Probabilistic Clustering, Association Rule Mining, Apriori Algorithm, f-p Growth Algorithm, Gaussian Mixture Model are described in this chapter with their practical use. Chapter 4 describes the various statistical learning theories used in machine learning. This chapter describes statistical learning theories such as Feature Extraction, Principal Component Analysis, Singular Value Decomposition, Feature Selection - feature ranking and subset selection, filter, wrapper and embedded methods, Evaluating Machine Learning Algorithms and Model Selection. Chapter 5 describes Semi-Supervised Learning and Reinforcement Learning. This chapter describes Markov Decision Process (MDP), Bellman Equations, Policy Evaluation using Monte Carlo, Policy Iteration and Value Iteration, Q-Learning, State Action-Reward-State- Action (SARSA) and Model-Based Reinforcement Learning. Chapter 6 describes the recommended system and basic introduction to neural networks and deep learning. This chapter includes various techniques used for the recommended system such as Collaborative Filtering and Content-Based Filtering. It also covers the basic introduction of Artificial Neural Network, Perceptron, Multilayer network, Backpropagation and introduction to Deep Learning.
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