MACHINE LEARNING Foundations, Algorithms, and Applications

This book provides a comprehensive introduction to Machine Learning, covering both fundamental concepts and advanced techniques. It begins with the basics of machine learning, including supervised, unsupervised, and reinforcement learning, followed by the mathematical foundations such as linear algebra, probability, statistics, calculus, and information theory. The book then explores key machine learning algorithms including regression, classification, clustering, ensemble learning, neural networks, deep learning, CNNs, RNNs, transformers, large language models, GANs, diffusion models, reinforcement learning, and graph neural networks.