This book is an in-depth guide designed to equip readers with both foundational knowledge and advanced understanding of modern machine learning. It navigates the reader through the key building blocks of ML—ranging from classical algorithms like linear regression and decision trees to cutting-edge deep learning models such as transformers and generative adversarial networks (GANs). By grounding complex ideas in clear explanations and practical examples, the book serves as a bridge between theory and implementation.

Whether you're a student building a solid conceptual base, a researcher deepening your technical expertise, or a practitioner applying ML in industry, Mastering Machine Learning offers a rich, multidimensional roadmap to becoming proficient in building intelligent, responsible, and high-performing machine learning systems.

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