This is not a get-rich-quick blueprint. It is a rigorous, honest exploration of how machine learning actually works when applied to financial markets. From raw data collection and feature engineering to model selection, backtesting, and live deployment, you will learn the practical steps for building predictive systems — and the many ways they fail. No hype, no guaranteed returns. Instead, you get realistic frameworks for evaluating alpha, managing overfitting, handling slippage, and surviving the brutal statistics of active trading. Whether you are a developer curious about quant finance or a trader wanting to understand what AI can and cannot do, this book teaches you to think like a machine learning engineer in markets. You will discover why most algorithmic strategies lose money, how to design robust experiments, and when it makes sense to walk away. Drawing from real-world pitfalls and proven principles, Nathan Cole Reeve delivers a clear-eyed guide that prioritises survival over promises. Part of The AI Trading Library.