What an AI model is and why learning from data matters — the roadmap for supervised, unsupervised, neural nets, and evaluation.

Labeled training with an answer key: classification into discrete categories and regression for continuous values, with email, weather, and house-price examples.

No labels — clustering similar items and association rules like bread and butter for recommendations.

Brain-inspired layers of nodes with weights and bias that automatically extract features.

A single neuron decides whether to go to the park using weighted inputs, bias, and a threshold.

Classroom activity where students act as layers passing messages — and training means fixing wrong guesses.

Accuracy, error, and confusion matrices as a model report card — and why the right metric matches real stakes.

Synthesize how models learn, decide, and get measured so you can ship AI you trust.