nlp_course
A university course on natural language processing, from word embeddings to LLM agents, as Jupyter notebooks.
- Stars
- 10.8k
- Forks
- 2.8k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 7 days ago
- Type
- Learning resource
What it is
This is the lecture and seminar material for a Natural Language Processing course run by the Yandex School of Data Analysis. It is organized into weekly folders covering topics from word embeddings and language modeling through transformers, transfer learning, large language models, prompting, fine-tuning, efficiency, retrieval-augmented generation, and AI agents. People use it to learn NLP step by step, with notebooks for seminars and homework alongside the lecture content.
- Work through the week01 seminar on word and sentence embeddings
- Build an n-gram language model from scratch using the week02 seminar
- Follow the week04 homework on fine-tuning a pre-trained BERT model
- Read the interactive lecture materials linked from the syllabus
Great for
- Students who want a structured path through modern NLP
- Developers moving from basic machine learning into language models
- Self-learners who can work through Jupyter notebooks at their own pace
- Instructors looking for an existing course outline and materials
Not ideal for
- People wanting a ready-made software library or app, since this is course material rather than a tool (the README does not describe a packaged product)
- Complete beginners to machine learning, since the syllabus assumes prior ML background (inferred from topics, not stated explicitly)
How to get it
- 1
Clone and build from source
Clone the repository
git clone https://github.com/yandexdataschool/nlp_course - 2
Run it
- Clone the repository locally
- Open the week folder you want to study, for example week01_embeddings
- Follow the lecture and seminar notebooks in that folder
- Check the linked issue thread for library installation and troubleshooting help
- 3
Get the most out of it
- Use the interactive lecture links in the syllabus alongside the notebooks
- Read the per-week README files for materials and instructions, as the main README points there
- Report bugs or suggest improvements through the project's issue tracker
Downloads
Strengths
- Covers a broad range of NLP topics from classic methods to current LLM agents
- Lecture material is linked to interactive online content by Elena Voita
- Released under the MIT license
Trade-offs
- The README says the 2025 iteration is being added as materials are prepared, so some weeks may be incomplete
- The README is a syllabus and staff list; setup details are in per-week materials and a linked issue thread, not in the README itself
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.