📄️ Table of contents
Free discovery course on Natural Language Processing in 6 lessons. Why language is hard for computers, what tokenisation is, how embeddings capture meaning, which NLP tasks are solved, and where the field still fails.
📄️ 1. Why language is hard
Lesson 1 of the free NLP course: the five properties of language that resist computation — ambiguity, context dependence, implication, variation and open vocabulary — and why rule-based grammar failed.
📄️ 2. Tokenisation
Lesson 2 of the free NLP course: why splitting on spaces fails, what subword tokenisation solves, why models count tokens rather than words, and why some languages cost more tokens for the same content.
📄️ 3. Embeddings
Lesson 3 of the free NLP course: from word counting to learned vectors, why similarity becomes a dot product, why contextual embeddings replaced fixed ones, and what embeddings are used for in production.
📄️ 4. The tasks
Lesson 4 of the free NLP course: classification, named entity recognition, translation, summarisation, question answering and speech, with an honest assessment of which are production-ready and which need review.
📄️ 5. Where it still fails
Lesson 5 of the free NLP course: why models fabricate facts, how bias enters through language data, why thousands of languages are poorly served, and why benchmark scores overstate real performance.
📄️ 6. Recap and FAQ
Lesson 6 of the free NLP course: a one-page synthesis, a table matching business needs to NLP approaches, a glossary, and answers to the 12 questions asked most about natural language processing.
📄️ 7. Quiz and attestation
Test your understanding of natural language processing with a free 5-question quiz, corrected and explained: tokenisation, embeddings, why word order matters, the main NLP tasks and their limits.