Feature engineering, pipeline construction, and model evaluation metrics introduce classical regression and classification before deep-learning concepts are demystified through autodiff and GPU batching. Unsupervised learning, graph neural networks, hyperparameter optimisation, and model deployment are covered end-to-end, highlighting reproducibility, fairness, and interpretability considerations essential for mathematically rigorous AI development.