Machine Learning Fundamentals provides a comprehensive overview of data science, emphasizing machine learning (ML). This book covers ML fundamentals, processes, and applications, that are used as industry standards. Both supervised and unsupervised learning ML models are discussed.

Topics include data collection and feature engineering techniques as well as regression, classification, neural networks (deep learning), and clustering. Motivated by the success of ML in various fields, this book is designed for a wide audience coming from various disciplines such as engineering, IT, or business and is suitable for those getting started with ML for the first time.

This text can also serve as the main or supplementary text in any introductory data science course from any discipline, offering real-world applications and tools in all areas.

About the Authors

Dr. Amar Sahay is a professor engaged in teaching, research, consulting, and training. He has a BS in production engineering (BIT, India), MS in industrial engineering and a PhD in mechanical engineering from University of Utah. He has taught/teaching at several Utah institutions including the University of Utah (school of engineering/ management), Weber State University, SLCC, Westminster College, and others. Amar is a Six Sigma Master Black Belt and certified in lean manufacturing. He has over 30 research papers in various conferences. Amar is the author of 11 books and is a senior member of Industrial & Systems Engineers, American Society for Quality, and Data Science Central.

Dr. Rajeev Sahay is an Assistant Teaching Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. He earned his PhD in Electrical and Computer Engineering in December 2022 from Purdue University. Dr. Sahay has extensive teaching experience in machine learning, data science, and programming languages in C, C++, and Python. His research interests lie in the intersection of machine learning and networking, particularly in their applications for educational data and wireless communications.

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