Machine Learning Foundations

An intuition-first foundation in classical machine learning for anyone moving into LLM and agent work. It assumes no machine learning at all, only everyday Python, and it is built to do one thing well: give you enough real understanding to discuss ML concepts with confidence and to frame working ML projects from a domain you already know.

You will learn what machine learning actually is, the workflow every project follows, the handful of algorithms worth knowing as intuition, how to turn the world into features, and above all how to evaluate a model honestly and avoid the traps that fake good results. The emphasis is on understanding and judgment, not derivations: you reason, explain, and decide, with light hands-on scikit-learn where running something beats reading about it.

Every idea is tied to the work you are heading toward, because modern LLM and agent evaluation is classical ML evaluation in new clothes. By the end you can hold a real conversation about overfitting, precision and recall, baselines, and data leakage, and you can look at a problem from your own experience and frame it as a sensible, working ML project.