Machine learning is reshaping financial markets, but separating genuine strategy from hype has never been harder. This book gives you a clear, no-nonsense path to understanding and applying ML to trading — without promising instant riches. You'll start with the core concepts of supervised and unsupervised learning, then move through feature engineering, model selection, backtesting pitfalls, and practical deployment. Each chapter builds on the last, using real-world market data and open-source tools. You'll learn how to avoid common errors like look-ahead bias and overfitting, and how to interpret model outputs responsibly. This isn't about turning a small stake into a fortune; it's about gaining a realistic edge while managing risk. Whether you're a curious investor or a developer exploring quant finance, this book equips you with the knowledge to design, test, and critique your own ML-based trading systems. Expect honest discussions of limitations, regulatory concerns, and the fact that most algorithmic traders lose money. Part of The AI Trading Library.