This book on machine learning is designed for students and researchers, covering current topics and providing theoretical groundwork, conceptual tools, and practical applications. It introduces innovative theoretical tools and concepts, addressing complex issues and ongoing research areas. The book covers advanced techniques in supervised, unsupervised, and reinforcement learning with practical examples for clarity. Each chapter builds on foundational knowledge, starting with core principles in Chapter 1 and a comprehensive overview of data and statistics in Chapter 2. Chapters 3 and 4 explore supervised and unsupervised learning algorithms and applications. Chapter 5 introduces reinforcement learning, Chapter 6 focuses on model evaluation and selection, and Chapter 7 examines hyperparameter tuning and model selection strategies. Chapter 8 discusses advanced supervised learning techniques like ensemble methods and self-supervised learning. The book aims to equip readers with a thorough understanding of machine learning, assuming a foundational knowledge of statistics, probability, and algorithm analysis and emphasizes proofs and theoretical underpinnings.