The book Fundamentals of Machine Learning & Deep Learning provides a structured introduction to machine learning and deep learning concepts. It starts with Bayesian Decision Theory and foundational ML concepts, progressing to advanced topics like neural networks, ensemble learning, and deep learning applications. Core topics include classification, regression, clustering, optimization, CNNs, RNNs, and reinforcement learning. The book balances theory with practical examples, making it a valuable resource for students, researchers, and industry professionals. By the end, readers gain a strong grasp of ML and DL principles, enabling them to apply these techniques to real-world problems.
