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Machine Learning changes businesses and lifestyles. A Practitioner's Guide for Machine Learning combines theory and practice for beginners and experts.
Machine Learning foundations, relevance, and applications are covered. Chapters end with applying principles to life.
Python Fundamentals, the second chapter, teaches model-building programming.
Data preprocessing includes cleansing, transformation, and feature engineering. Next, we explore linear regression, decision trees, clustering, and dimensionality reduction in supervised and unsupervised learning.
Machine Learning teaches model performance measurement and improvement, whereas Introduction to Neural Networks and Deep Learning covers sophisticated topologies.
Language Processing and Reinforcement Learning applications are covered in this book. Through case studies and ethics, real-world applications show impact.
Future Directions predicts advanced themes and trends to enhance learning in a changing industry.
About 50 Python programs in this book demonstrate Machine Learning in business, finance, agriculture, health, neurology, NLP, and games. These examples explain Machine Learning and show its versatility and strength across fields.
Sidebars enhance learning by providing context.
This book integrates theory and practice to help you explore Machine Learning, a growing topic.
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Dr. Sellappan Palaniappan, Dr. Sumit Dhariwal
Dr. P. Sellappan Palaniappan is a Professor of Information Technology at HELP University. He holds a Bachelor of Economics (Statistics) from the University of Malaya, an MSc in Computer Science from the University of London (UK), and a PhD in Interdisciplinary Information Science from the University of Pittsburgh (USA). He has taught diverse undergraduate and postgraduate courses, including Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, and Software Engineering. His current research includes applying AI and Machine Learning across diverse domains such as cybersecurity, healthcare, retail, finance, education, agriculture, manufacturing, logistics, climate, and sustainable development goals. He has supervised over 25 PhD and 30 MSc theses,
published 100+ research papers, and authored several IT textbooks. He actively contributes to the academic community as an external examiner, keynote speaker, and editorial board member. His professional afiliations include the Chartered Engineering Council (UK), the British Computer Society (UK), and Malaysia Board of Technology.
Dr. Sumit Dhariwal is an Assistant Professor at the Centre for Artificial Intelligence, Madhav Institute of Technology & Science (MITS-DU), a government organization in Gwalior, Madhya Pradesh, India. He earned a Doctorate from MUST University Malaysia, an M. Tech from Samrat Ashok Technological
Institute Vidisha in Madhya Pradesh, and a B. Tech in Computer Science and Engineering from State Technical University in Madhya Pradesh, India. His research focuses on enhancing image processing categorization, computer vision, artificial intelligence/machine learning, green technology, crop disease detection, and cyber security. His research interests include crop disease detection, cyber security, cloud development methodologies, human crowd behavior analysis, and live face detection model enhancement. He has published over 30 research publications in reputable journals (SCI/SCIE/SCOPUS) and conferences. Dr. Dhariwal has served as an expert speaker in a variety of Faculty Development Programmes and has conducted numerous conferences, FDPs, workshops, and short-term courses.
