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Natural Language Processing

Working with text: tokens, embeddings and transformers.

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This course has 3 lessons. Start with the first one and use the next / previous links at the bottom of each lesson — the sidebar keeps the whole course in order.

Lessons

  1. Text preprocessing and tokensTurn raw text into clean, tokenised input: normalisation, a sparse baseline, and how subword tokenizers actually split words.
  2. Word and sentence embeddingsWhy dense vectors replaced sparse counts, how cosine similarity works, and how to use sentence embeddings for real retrieval.
  3. Transformers and fine-tuning basicsWhat self-attention computes, how encoder and decoder stacks differ, and when fine-tuning is worth the cost compared with prompting.

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Last refreshed 2026-09-17.