Understanding Machine Learning Basics and Beyond

by Prof. Sheetal Chavan, Pallavi Uttamrao Patil, Shamal Vijay Sonawane

This book is authored by Prof. Sheetal Chavan is currently working as an Assistant Professor at Genba Sopanrao Moze College of Engineering, Pune. She teaches subjects including IoT and Web Application Development and has made a significant impact by guiding students and contributing to cultural events. In addition to her teaching role, she serves as a Training and Placement Cell Coordinator. Sheetal is also skilled in quality assurance, having worked for Varroc Engineering Pvt. Ltd., where she earned the prestigious 'Best of Best' Quality Circle Award.Pallavi Uttamrao Patil is an Assistant Professor in the Electronics Department at Dr. D.Y. Patil ACS College, Pimpri, Pune, where she has been serving since 2019. She holds an M.E. in Digital Systems from JSPM’s RSCOE, Pune, and a B.E. in Electronics and Telecommunication from N.M.U, Jalgaon. Her research interests include digital image watermarking and data hiding techniques, with several publications in reputed journals and conferences. She has also worked as a visiting lecturer at Alard College of Management and Engineering and Bharati Vidyapeeth COE, Pune.Shamal Vijay Sonawane is an experienced academician specializing in Electronics and Telecommunication Engineering. She holds a Master of Engineering (M.E.) in Digital Systems from Pune University and has a strong background in Instrumentation Systems, Computer Organization, Digital Communication, Wireless Communication, and IoT. With multiple years of teaching experience at Dr. D.Y. Patil ACSC, PiThis book provides a comprehensive exploration of machine learning, covering fundamental concepts, advanced techniques, and real-world applications. Beginning with an in-depth introduction to machine learning, it systematically explains different learning paradigms, including supervised, unsupervised, and reinforcement learning. The book emphasizes essential topics such as data preprocessing, feature engineering, and model evaluation, ensuring readers develop a strong foundation in building and optimizing machine learning models. It also delves into deep learning and neural networks, highlighting their transformative impact on fields like computer vision and natural language processing.

In addition to technical aspects, this book addresses the practical challenges of deploying machine learning models in production environments. It discusses the ethical implications of AI, emphasizing the importance of explainability, fairness, and responsible AI development. Through structured explanations, case studies, and insights into the latest advancements, this book serves as a valuable resource for students, researchers, and professionals looking to understand and apply machine learning effectively.

107 pages, Paperback

Published April 2, 2025