Machine Learning Fundamentals: From Examples to Trustworthy Models

Most machine learning in production is a supervised model on a table of features, and most of its failures are in evaluation rather than in the algorithm. This path teaches how a model is fitted from examples, how to measure what it learned without fooling yourself, how to judge a classifier when the event you care about is rare, and why tree ensembles remain the default on tabular data. Every chart uses numbers you can reproduce in scikit-learn.