Machine Learning Handbook

This handbook offers a conceptual introduction to machine learning, intended for readers from diverse academic and professional backgrounds. It emphasizes understanding over implementation, focusing on the intuition behind key algorithms and methods—such as classification, regression, Bayesian models, and neural networks—without relying on programming or formal mathematics. Through illustrations, examples, and explanations, it serves as both a starting point for newcomers and a reference for common approaches in the field. The objective is to support interdisciplinary research by making machine learning concepts more accessible, encouraging collaboration across fields, and fostering more informed and reflective integration of these tools into various domains.