The core machine learning workflow, supervised vs. unsupervised learning, the train/validation/test split, overfitting and the bias-variance tradeoff, classical algorithms and when to use them, and the evaluation metrics that keep a model's real performance from being overstated.
Module 1 framed machine learning as the ring inside AI that learns patterns from data instead of following hand-written rules. This module makes that concrete: the actual workflow from data collection through feature engineering, training, validation, and testing; the difference between supervised learning (classification and regression) and unsupervised learning (clustering and dimensionality reduction); and why a model's accuracy on its own training data can be a dangerously misleading number on its own.
By the end of this module you should be able to explain the bias-variance tradeoff in your own words, name the classical algorithms — linear and logistic regression, decision trees, random forests, gradient boosting — that solve most structured-data problems without ever needing a neural network, and know why accuracy alone is misleading on imbalanced data. Module 3 (Deep Learning) picks up exactly where this module leaves off, once the "model" specifically becomes a neural network.