Machine Learning Foundations

What AI and ML are, how learning differs from rules, and how to choose a first path.

Write small Python programs and reason about arrays, shapes, vectorization, and reproducible experiments.

Inspect real data, handle missing values, encode features, avoid leakage, and build repeatable transformations.

Frame a problem, split data correctly, choose metrics, detect overfitting, and compare against a baseline.

Predicting continuous values using a linear relationship.

Build an interpretable binary classifier using logits, sigmoid probabilities, cross-entropy, and thresholds.

Translate fairness, privacy, safety, transparency, and human oversight into testable design decisions.