Ten topics: supervised learning foundations and the generalisation goal, linear regression with its assumptions and regularised variants, logistic regression and interpretation, k-nearest neighbours, Naive Bayes, decision trees and overfitting control, support vector machines and the kernel trick, multi-class and multi-label strategies, probability calibration, and a closing mastery topic on algorithm comparisons and when to use what.

Anyone preparing for data-science or ML interviews who can fit a model but would hesitate if asked why logistic regression beats a tree on a small, linearly separable dataset — or what calibration is for. It assumes basic probability and comfort reading a formula. Deep learning is out of scope.

Machine Learning: Foundations covers framing, data preparation and evaluation discipline. Supervised Mastery goes deep on the labelled-data algorithms. Unsupervised Mastery covers clustering, dimensionality reduction and anomaly detection. Ensemble Methods & Mastery covers bagging, boosting and stacking. Foundations first, then any of the other three in whatever order your work or interviews demand.

The course is organised as topics, subtopics, and individual lessons. Each lesson pairs a short set of explanation cards with practice questions written for that lesson, so the questions test the idea you just read rather than a general survey. Wrong answers come with a worked explanation, and the AI guide surfaces the mistakes you keep repeating.