AI and Machine Learning Intro

Learn supervised and unsupervised learning, evaluation metrics, and bias/variance; build intuition with simple models before advancing.

Understand layers, activation, loss, and optimization; experiment with small networks to see how representation learning emerges.

Explore tokenization, embeddings, sequence models, and transformers; build mini‑projects like text classification and summarization.

Consider fairness, transparency, and real‑world impact; document data provenance and risk mitigations in project notes.

Progress from conceptual courses to implementation; maintain reproducible code and written explanations; join communities for feedback.