Comprehensive open interactive course covering Transformer models, tokenization pipelines, datasets, fine-tuning, and the open-source LLM ecosystem.
The official Hugging Face LLM Course provides a structured walkthrough of modern natural language processing and large language models. Starting with how encoder, decoder, and encoder-decoder Transformers process text, it covers tokenization internals, dataset curation, supervised fine-tuning, and model sharing.
Course status is tracked independently from roadmap topic checkboxes so you can mark external courses without altering your topic mastery percentage.
Phase 4 Primary Starting Course (Modules 4.1, 4.2 & 4.5)
100% free interactive web chapters, quizzes, and runnable Colab/Kaggle notebooks.
Status: Verified (2026-11-09)
Review these foundations before starting so you get the most out of this material:
In Phase 4: NLP & Language Modeling
Start Phase 4 here (Chapters 1 & 2) to understand how raw text flows through tokenizers, embedding layers, and language modeling heads.
Unicode code points, UTF-8 byte streams, normalization forms, sentence segmentation, corpus cleaning, and vocabulary construction.
Bag of Words, TF-IDF weighting, N-gram statistical language models with smoothing, and dense static embeddings (Word2Vec, GloVe).
Recurrent Neural Networks (RNNs), LSTMs, teacher forcing, seq2seq encoder-decoders, and the information & parallelization bottlenecks that led to Transformers.
Dedicated technical module diving deep into subword tokenization algorithms: Byte-Pair Encoding (BPE), WordPiece, Unigram, normalization, pre-tokenization, and building tokenizers from scratch.
Stanford's flagship graduate NLP curriculum covering word vectors (Word2Vec), neural language models, recurrent networks (RNN/LSTM), attention mechanisms, and pretrained Transformers.
A code-first introduction to neural networks covering automatic differentiation, backpropagation, language models, and GPT implementations.