Machine learning has transformed how market participants analyze data, but separating hype from genuine edge is harder than ever. This book gives you a practical, no-nonsense roadmap to designing, testing, and deploying ML-based trading strategies—without promising easy riches. You will learn how to clean and engineer financial features, select appropriate models (from linear regression to LSTMs and transformers), and rigorously evaluate performance using walk-forward and cross-validation methods that respect market structure. Crucially, you will confront the real-world pitfalls: overfitting, survivorship bias, regime shifts, and the painful truth that most strategies fail to generalize. The second half guides you through building automated pipelines for live trading, including risk controls, execution logic, and monitoring. Written for developers, quantitative analysts, and curious traders, this book demystifies the hype and equips you with a repeatable process—not a magic formula. You will emerge with a clear framework to build your own systems and a healthy scepticism for anyone claiming easy profits. Part of The AI Trading Library.