Machine Learning

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.

By the end of this book, the reader can understand Machine Learning concepts and easily implement various ML algorithms to real-world problems.

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.

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.
* Learn about Artificial Neural Network along with the Back Propagation Algorithm.
* Make use of various recommended systems with powerful algorithms.

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 cases. Knowing basic statistical and programming concepts would be good, although not mandatory.

1. Introduction
2. Supervised Learning Algorithms
3. Unsupervised Learning
4. Introduction to the Statistical Learning Theory
5. Semi-Supervised Learning and Reinforcement Learning
6. Recommended Systems

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 Azteca University, Mexico, and as an adjunct faculty at the Jyoti Vidyapeeth Women’s University, Jaipur, Rajasthan, India. She has also worked with the BlueCrest University College, Liberia, West Africa as a Registrar and Head, Examination; BlueCrest University College, Ghana, Africa; Amity University, Rajasthan, India; Trimax IT Infrastructure & Services, Udaipur, India.

LinkedIn Profile: Dr Ruchi Doshi

Kamal Kant Hiran works as 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, Denmark. He is a Gold Medalist in M.Tech. (Hons.). He has more than 16 years of experience as an academic and researcher in Asia, Africa, and Europe. He worked as an Associate Professor and Head of 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; Visiting Lecturer at the Government Engineering College, Ajmer.

LinkedIn Profile: Kamal Kant Hiran

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 the Department at S. S. College of Engineering. Udaipur; Assistant Professor at Sobhasaria Engineering College, Sikar; Lecturer at the Institute of Technology & Management, Bhilwara.

He is a reviewer of international peer-review journals. He is the author of several research papers in peer-reviewed international journals and conferences.

LinkedIn Profile: Ritesh Kumar Jain

Dr. Kamlesh Lakhwani works as an Associate Professor, in Computer Science & Engineering at JECRC University Jaipur, Rajasthan, 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 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 more than 50 research papers in the conferences/journals/ seminars of International and National repute. His area of interest includes Cloud Computing, theInternet of Things, Computer vision, Image processing, video processing, and Machine Learning.

LinkedIn Profile: Dr. Kamlesh Lakhwani