Whether based on academic theories or discovered empirically by humans and machines, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory. Unlike conventional AI systems, probabilistic Machine Learning (ML) systems treat errors and uncertainties as features, not bugs. They quantify uncertainty generated from inexact model inputs and outputs as probability distributions, not point estimates. Probabilistic ML is the next generation ML framework and technology for AI-powered financial and investing systems for many reasons. They are generative ensembles that learn continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, prediction and counterfactual reasoning. In this book, I am assuming that readers have a basic familiarity with finance, statistics, Machine Learning, and Python. I am not assuming that they have read any particular book or mastered any particular skill. I am only assuming that they have a willingness to learn, especially when ChatGPT, Bard, and Bing AI can easily explain any code or formula in this book.