Learn NLP

Teach computers to work with words, one useful idea at a time.

Start learning 6 lessons · about 1.5 hours · free
About this track

Explore how computers process and learn from human language. Start with text and tokenization, then build word-count and classification baselines, learn n-grams, and connect embeddings and Transformers. Every lesson includes a check, quiz, and hands-on Python exercise that runs in your browser.

Before you start
  • No prior NLP experience needed.
  • Basic Python helps; examples introduce the code as you go.
  • A modern browser with JavaScript enabled.
  1. Unit 1 · 0/2 lessons

    Text foundations

    Normalize text and choose how to split it into tokens. Badge: Token tamer

    1. 0x001Turn text into tokensNormalize text and split it into pieces a language system can process. 14 min 0/1 exercises solved 14 min 0/1 exercises solved
    2. 0x102Choose a tokenization strategySplit sentences consistently and make punctuation handling explicit. 14 min 0/1 exercises solved 14 min 0/1 exercises solved
  2. Unit 2 · 0/3 lessons

    Classical NLP methods

    Count words, build a transparent baseline, and learn from short sequences. Badge: Pattern finder

    1. 0x203Represent documents with word countsBuild a bag-of-words view and learn what frequency leaves out. 14 min 0/1 exercises solved 14 min 0/1 exercises solved
    2. 0x304Classify text with simple featuresBuild a tiny sentiment baseline and learn to evaluate it fairly. 14 min 0/1 exercises solved 14 min 0/1 exercises solved
    3. 0x405Model short sequences with n-gramsCount nearby word sequences and explore what context can tell us. 14 min 0/1 exercises solved 14 min 0/1 exercises solved
  3. Unit 3 · 0/1 lessons

    Embeddings and Transformers

    Connect vector representations to attention and language models. Badge: Context builder

    1. 0x506Connect embeddings, attention, and language modelsRelate vector representations to context-aware Transformer models. 14 min 0/1 exercises solved 14 min 0/1 exercises solved
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