Overview

The first volume of A Practitioner's Journey. Eighteen chapters take you from linear algebra and probability through every classical machine-learning algorithm worth knowing - regression, trees, ensembles, SVMs, KNN, time series, and recommendation systems - with the math, the intuition, and runnable code, all in one place. This is the curriculum a working ML practitioner actually needs. Most ""intro to ML"" books pick a side: pure math with no code, or library-call tutorials that fall apart the moment you try to apply them. Foundations of Machine Learning refuses both. Every chapter is built around a working scenario. Every code example runs. Every concept comes with both the math and the intuition. You will learn to: Reason about linear algebra, calculus, probability, and optimization the way ML uses them Derive and implement classical algorithms from first principles, not as library calls Choose the right algorithm for the right problem and explain why Evaluate models honestly, avoid overfitting, and know when ""good enough"" is good enough Apply the CRISP-DM framework to a real end-to-end case study Companion volumes: Book 2 Machine Learning in Production covers deep learning, computer vision, and the production engineering stack. Book 3 Artificial Intelligence in Production covers LLMs, RAG, agents, and modern AI infrastructure.

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