Authors: Dr. D. David Neels Ponkumar, Dr. Parthiban Aravamudhan, Dr. R. Julian Menezes, Mrs. Pavithra Karthik, and Dr. Ramya Nimmagadda

ISBN: 978-81-997985-8-8

DOI: https://doi.org/10.59646/571

Date of Publication: January 28, 2026

Preface

Artificial Intelligence and Machine Learning have moved decisively from theoretical constructs to foundational technologies shaping how societies function, industries operate, and decisions are made at scale. Applications of Artificial Intelligence and Machine Learning is designed to present a comprehensive, application-centric exploration of this transformation, bridging core concepts with real-world implementation across diverse domains. Rather than treating AI and ML as abstract algorithms, this book emphasizes how intelligent systems are designed, deployed, and evaluated in practical settings such as language understanding, computer vision, robotics, business intelligence, healthcare, education, agriculture, cybersecurity, and beyond. Part I focuses on the applications of Artificial Intelligence, offering in-depth coverage of natural language processing, speech systems, vision and perception, robotics, autonomous systems, and intelligent decision-making in business and society, while also addressing ethical, legal, and governance frameworks essential for responsible AI adoption. Part II extends this foundation by examining the role of Machine Learning and Deep Learning in powering advanced applications, with detailed discussions on computer vision, NLP, deep learning architectures, and representative case studies that demonstrate end-to-end system design. The structured unit-wise approach enables progressive learning, moving from fundamentals to advanced applications, and is supported by practical examples, conceptual clarity, and interdisciplinary perspectives. This book is intended for undergraduate and postgraduate students, educators, researchers, and professionals seeking a clear, systematic, and application-oriented understanding of AI and ML technologies. By aligning theoretical principles with contemporary use cases and emerging challenges, the book aims to equip readers with the knowledge and insight required to design intelligent solutions, critically evaluate AI-driven systems, and contribute meaningfully to the evolving landscape of intelligent computing.