Mathematics for Machine Learning and AI

Mathematics for Machine Learning and AI provides a foundational and practical understanding of the core mathematical concepts that underpin modern artificial intelligence systems. It covers essential topics such as linear algebra calculus probability theory statistics optimization and discrete mathematics all tailored to their applications in machine learning and AI. This book bridges the gap between mathematical theory and practical implementation making complex topics accessible through clear explanations real-world examples and hands-on problem-solving. Readers will learn how eigenvalues gradients probability distributions and optimization algorithms drive intelligent systems-from neural networks and decision trees to deep learning and reinforcement learning. Designed for students educators and professionals the book balances theoretical rigor with intuitive insights offering both the mathematical depth and applied knowledge needed to excel in the evolving fields of data science AI and machine learning.