AIML 112

Machine Learning I

Supervised Learning: Theory, Algorithms and Practice

A lender in Kano must decide which loan applications to approve. A telecom operator wants to know which subscribers are about to leave. A family in Ibadan wants to know whether the rent they are being asked for is fair. Each of these is a prediction from data, and each can be made well or badly. Machine learning is the discipline of building such predictions from examples, measuring how good they are, and understanding when they can be trusted. This book teaches its core: supervised learning, in which a model learns to predict a known outcome from labelled examples.

14 chapters, 291 pages. Published by RAIN, 2026. Full contents