BERT training pipeline details: tokenizer creation, dataset construction with NSP, masking strategies, segment handling, and truncation.

Practical applications of BERT for sentiment analysis, relation extraction, semantic similarity, NER, and machine reading comprehension.

Hands-on regex practice dissecting a password validation pattern with lookaheads and character classes.

Summary of language model benchmarks from GLUE to multilingual evaluations, covering the evolution from RNN to ELMo, BERT, and beyond.

Discussion of AI bias, fairness, and privacy issues, including the COMPAS case study and COVID contact tracing privacy analysis.

What it means to be a full-stack ML engineer: roles, ML product lifecycle, team structure, and trade-offs.