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Guide · Open source

How to use nlp_course: clone, study weekly materials and run notebooks

Updated · written with AI from the project's own documentation and checked against our catalogue data

nlp_course is the lecture and seminar material for the YSDA Natural Language Processing course, published as a GitHub repository of Jupyter notebooks organized by week. This guide explains how to get a copy, find your way through the syllabus from word embeddings to AI agents, and where to look when something breaks. It is aimed at students, developers and self-learners who want to study modern NLP step by step.

nlp_courseyandexdataschool/nlp_course · ★ 10.8k · MIT

Quick start

  1. 1Clone the repository. Run the git clone command from the README to copy nlp_course to your machine.
  2. 2Read the syllabus. Open README.md to see each week's lecture topics, seminar and homework.
  3. 3Start with week01. Open the week01_embeddings folder and work through the lecture materials and seminar on word embeddings.
  4. 4Run the seminar notebooks. Open each seminar or homework notebook in Jupyter and run the cells in order.
  5. 5Check the troubleshooting thread. If a library fails to install or a notebook errors out, look at the linked issue thread before asking elsewhere.
  6. 6Report problems or contribute. Open an issue on the GitHub repository for bugs, ideas or contribution suggestions.

Get a copy of the course repository

The repository is installed by cloning it. The README lists one install command, which copies the whole repository to your machine so you can open the notebooks locally.

The README does not list any prerequisites, so the only setup it describes is the clone itself. Installing the Python libraries the notebooks need is covered by a separate thread linked from the README.

  • Install method: clone the repository.
  • Prerequisites: none listed in the README.
git clone https://github.com/yandexdataschool/nlp_course

Understand how the materials are organized

Lecture and seminar materials for each week live in ./week* folders. Each syllabus entry links to its folder, so the repository layout mirrors the course schedule.

The README says that materials are added as they are prepared, and that this is the 2025 iteration. Check the folders you need before you plan your study, because some later weeks list only a lecture or a practice session.

  • week01: Word Embeddings, with a seminar on word and sentence embeddings and an embedding-based machine translation homework.
  • week02: Language Modeling, with a seminar that builds an N-gram language model from scratch.
  • week03: Seq2seq and Attention, covering the Transformer and BPE subword segmentation.
  • week04: Transfer Learning, with a homework on fine-tuning a pre-trained BERT model.
  • week05 to week11: Large Language Models, Prompting, Efficiency, Retrieval-Augmented Generation, AI Agents and Interpretability.
  • week12 to week14: Multimodal LLMs, Building LLM Systems and AI Agents in Production, listed in the syllabus without a detailed breakdown.

Study the lectures and seminars in order

The course is designed to be followed sequentially, since each week builds on earlier ideas. For example, the Transformer material in week03 comes before transfer learning in week04, and the efficiency topics in week08 assume the model material that came before.

Many lecture entries link to interactive lecture materials on the original course author's site. Use those links alongside the notebooks when a topic is hard to grasp from text alone.

  • Read the lecture outline for the week in the README.
  • Follow the interactive lecture link if one is provided.
  • Open the seminar notebook and run the cells yourself.
  • Attempt the homework, which is listed for most weeks.

Work through homework and practice notebooks

Most weeks include a homework task, such as building an embedding-based machine translation system in week01 or manual prompt engineering in week06. Some weeks list a practice session instead of homework, such as hands-on work with open-source LLMs in week05.

The README does not describe grading or submission for self-learners. Treat the homework as an exercise you complete locally in the notebooks.

Get help with installation and troubleshooting

The README points to a single thread for installing libraries and troubleshooting. Check it first when a notebook fails because of a missing package or an environment problem.

For bugs in course materials, contribution ideas or other technical issues, the README asks you to open an issue on the repository's GitHub page.

  • Library installation and troubleshooting: the linked GitHub issue thread (issue number 1 in the repository).
  • Bugs, ideas and contributions: open a new issue in the repository.

What people use it for

  • A student following a structured NLP course from word embeddings through transformers and LLMs.
  • A developer moving from classical machine learning into language models by working through the week-by-week notebooks.
  • A self-learner using the seminar notebooks, such as building an N-gram language model from scratch, at their own pace.
  • An instructor looking for an existing course outline and materials to adapt for their own class.

Frequently asked questions

Is nlp_course free to use?

The repository is licensed under MIT, according to the listing for this project. The MIT licence generally permits use, copying and modification, so check the LICENSE file in the repository for the exact terms.

What do I need before I start nlp_course?

The README does not list prerequisites. The syllabus, however, assumes some prior machine learning background, so it is best suited to people who already understand basic ML before starting.

How do I install nlp_course?

Clone the repository with git clone https://github.com/yandexdataschool/nlp_course. The README does not give a separate install step for the notebooks, and points to a GitHub issue thread for installing libraries.

Is nlp_course a Python library I can import?

No. It is course material made of lecture and seminar notebooks, not a packaged software library or app.

What is nlp_course compared to other NLP courses?

Comparable courses include Stanford CS224N, the fast.ai NLP course and the Hugging Face NLP Course. nlp_course stands out for its weekly notebook structure, which covers topics from word embeddings to AI agents.

What should I do if a notebook fails to run?

Check the linked GitHub issue thread for library installation and troubleshooting. If the problem is a bug in the course materials, open an issue on the repository.

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Our verdict

nlp_course suits students, developers and self-learners who want a structured, notebook-based path through modern NLP and who already have some machine learning background. It is not a tool or library, so people who need a ready-made software package or a beginner-friendly introduction to machine learning should look at other options such as the fast.ai NLP course or the Hugging Face NLP Course.

Install options & downloads