Authors: Dr. S. Murugesan and Ms. D. Meenakshi

ISBN: 978-81-987266-8-1

DOI: https://doi.org/10.59646/ml/351

Date of Publication: April 23, 2025

About the Book:

Machine Learning in the Age of AI: Foundations, Applications, and the Road to AGI offers an in-depth exploration of the evolving landscape of machine learning, blending foundational theories with emerging innovations to prepare readers for the future of artificial intelligence. Divided into five meticulously structured units, the book begins by building a strong understanding of the fundamentals—introducing core machine learning concepts, various forms of learning (supervised, unsupervised, reinforcement), and the crucial roles machine learning plays in AI, data science, and Industry 5.0. Readers are guided through practical models of regression, classification, neural networks, and powerful regularization and optimization techniques, with hands-on attention to real-world applications. Moving into unsupervised learning and high-dimensional data analysis, the book discusses clustering methods, dimensionality reduction techniques like PCA and ICA, and the significance of representation learning. Advanced topics such as probabilistic graphical models, Bayesian networks, Markov chains, and sequence modeling deepen the reader’s understanding of structured predictions. The final unit shines a spotlight on reinforcement learning, from value-based and policy-based methods to actor-critic algorithms and Monte Carlo tree search, while also offering a forward-looking perspective on quantum machine learning, AI-powered edge computing, ethical AI practices, and bias mitigation. The book concludes with visionary discussions on AGI (Artificial General Intelligence) and self-learning systems, encouraging readers to not only grasp current technologies but also critically engage with the ethical, technical, and societal challenges of building the future of intelligent systems. Rich with examples, illustrations, and case studies, this book is an indispensable guide for students, researchers, and practitioners aiming to master the art and science of machine learning in an AI-driven world.