From bags of words to embeddings and transformers.
Python
PyTorchHow machines read text: tokenisation, representations from bag-of-words to TF-IDF, embeddings that put words with similar meanings close together, text classification, named-entity recognition, and the transformers underneath every modern LLM. Including NLP for Indonesian, which has traps of its own.
Read a free lesson: From Words to VectorsEvery module and lesson in this track. Lessons marked free are open to everyone; the rest open once you own the track.
Every lesson in this track, with summariesFrom one-hot vectors to word embeddings to contextual embeddings — how machines represent the meaning of words.
Sentiment analysis, topic detection and ticket routing — from TF-IDF + logistic regression to fine-tuned transformers.
Named entity recognition, relation extraction, and turning free documents into structured data.
Encoders, decoders, BERT and GPT — the architecture that changed NLP, and how to use pretrained models.
Sentence embeddings, vector databases and evaluating retrieval — the foundation of modern search and RAG.
Slang, code-mixing, regional languages and resources like IndoNLU — closing with a capstone project.
The real one, from a lesson — it runs in your browser. No account needed.
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