"This book is written for B.Tech (UG) engineering students who want a solid foundation in ML concepts as well as hands-on skills in Python. The focus is on building intuition first, then connecting intuition to mathematics, and finally implementing the methods in a reproducible workflow. To keep the material practical, every chapter includes code-oriented discussion, common pitfalls, and engineering choices (feature engineering, validation, hyperparameter tuning, and deployment). The book avoids objective-type questions and MCQs and instead provides detailed explanations and end-to-end project case studies. All algorithms are discussed with their assumptions, strengths, and limitations so that you learn not only how to train a model but also how to trust, debug, and monitor it in real applications. "