Foundations of Machine Learning

This book provides a comprehensive yet practical introduction to the field of Machine Learning. It is structured across nine chapters, progressing from foundational concepts such as supervised, unsupervised, and reinforcement learning to advanced methods including deep neural networks, support vector machines, ensemble methods, and dimensionality reduction. Each chapter integrates key equations, detailed explanations, and practical examples to help readers build both theoretical understanding and applied skills. A unique feature of this book is the inclusion of over 80 practice problems and exercises designed to strengthen comprehension and encourage hands-on implementation. The text emphasizes clarity, progressive learning, and real-world applications, making it an ideal reference for undergraduate and postgraduate students, researchers, and professionals in computer science, data science, and engineering. With its balance of mathematical rigor and practical insights, the book aims to equip readers with the tools and confidence to apply machine learning techniques effectively in real-world problem solving.