# yandexdataschool/nlp_course: the YSDA NLP course as a public GitHub repository > The YSDA Natural Language Processing course publishes its lecture and seminar materials as Jupyter notebooks under an MIT licence, one folder per week. Here is what the repository actually contains, how to get a week running, and where it stops being the right tool. **yandexdataschool/nlp_course** — YSDA course in Natural Language Processing - Repository: https://github.com/yandexdataschool/nlp_course - Website: https://lena-voita.github.io/nlp_course.html - Stars: 10,705 · Forks: 2,775 - Language: Jupyter Notebook - License: MIT - Published: 2026-09-21 · Updated: 2026-09-21 · Language: en - Canonical page: https://hysenlabs.com/projects/yandexdataschool-nlp-course ## What the YSDA NLP course repository is, and who it is built for This is the course material for the YSDA Natural Language Processing course, published on GitHub as a set of weekly folders. The README describes it as the 2025 iteration, with materials added as they are prepared. Each week gets its own directory, week01_embeddings through week14_agents_production in the syllabus, and each contains lecture and seminar material plus a README with instructions. The audience is someone who already writes Python and wants a sequential path through the field rather than a reference. The syllabus runs in a deliberate order: word embeddings, language modeling, seq2seq and attention, transfer learning, large language models, prompting, parameter-efficient fine-tuning and RLHF, efficiency, retrieval-augmented generation, agents, interpretability, multimodal models, and finally building and running LLM systems. That ordering matters. Attention arrives before transformers, and prompting arrives after you have seen the models being prompted. It is not an introductory programming course and it is not a product. The README lists course staff and volunteers, and links to the on-campus grading system, but the repository itself is the material, not the course administration. ## How the week folders and notebooks are organised The structure is flat and predictable. The top level holds .gitignore, LICENSE, README.md, resources/, and the week directories; the listing given for the repository shows week01_embeddings through week05_llm at the top level, with later weeks described in the syllabus. Each week's README carries the materials and instructions for that week, so the top-level README is a syllabus and index rather than a setup guide. The primary language is Jupyter Notebook, which tells you what the working format is: you read a lecture notebook, then you fill in a seminar notebook. The syllabus separates these explicitly. Week 1 has a lecture on distributional semantics, count-based methods, Word2Vec and GloVe, a seminar on playing with word and sentence embeddings, and a homework on an embedding-based machine translation system. Week 3 has a seminar on a basic sequence-to-sequence model and a homework on machine translation with attention. The pattern repeats: concept in the lecture, implementation in the seminar, a larger build in the homework. Lectures also link out to interactive materials on lena-voita.github.io, which is the original course author's site. Those pages are part of the intended reading, not decoration, and the repository does not mirror them. ## Getting the YSDA NLP course materials onto your machine The README does not carry install steps. It points to a single GitHub issue, "Installing libraries and troubleshooting", as the place where library installation and troubleshooting live. That is the first thing to open, and it is also the honest answer to how to set the course up: the instructions are community-maintained in a thread, not in a pinned requirements file. The README gives no clone command, so the practical route is the repository page itself. The cleaned README does name the repository URL as https://github.com/yandexdataschool/nlp_course, and the issue link follows the same owner and name, so that is where the materials live. Once you have the files locally, the instruction the README does give is per week: the lecture and seminar materials for each week are in the ./week* folders, and the README.md in each folder holds the materials and instructions for that week. So the first real step is to open the folder for the week you want, in this case week01_embeddings, and read its README.md before opening any notebook. The top-level README is an index; the week README is the authority on what to run and in what order. For week 1 that means the lecture on distributional semantics, count-based methods, Word2Vec and GloVe, then the seminar on playing with word and sentence embeddings, then the homework on an embedding-based machine translation system. The homework is the point at which the environment has to be genuinely working rather than mostly working, which is why the troubleshooting issue is worth reading before you start rather than after something breaks. ## Where the repository format gets in your way The materials are added as they are prepared, which the README states plainly. That means the syllabus is a plan, not a guarantee that every listed week is complete in the branch you cloned. A fourteen-week syllabus with materials still arriving is a moving target, and if you are planning a study schedule around a specific late week, you should check that week's folder exists before committing to it. Environment drift is the second issue. Because installation lives in an issue thread rather than a lockfile, the exact versions that made a notebook run at the time it was written are not recorded in the repository. Notebooks that train models are sensitive to library versions in a way that plain scripts are less exposed to, and a thread of user reports is a weaker contract than a pinned environment. The third limitation is that this is a course, not a library. There is no package to install, no API to call, and no versioned release to depend on. If what you need is a maintained tokenizer or a training framework, this repository is the wrong shape entirely. It also assumes you want the full sequence. Someone who only needs retrieval-augmented generation has to decide whether to skip ahead to week09_retrieval or work through ten weeks of prerequisite material first. ## What the materials assume you already know The homework in week 1 is an embedding-based machine translation system. That is a strong signal about the floor. Before you reach it you are expected to be comfortable with PyTorch-style tensor code, training loops, and reading a notebook that does not explain every line. The lecture material supplies the concepts, not the programming basics. The progression also assumes you will do the seminars. Week 2 asks you to build an n-gram language model from scratch before the neural language model homework, and week 3 asks for a basic sequence-to-sequence model before attention. Skipping the seminars and reading only the lectures turns a hands-on course into a slide deck, and the later weeks, particularly the fine-tuning and efficiency ones, depend on the habits built early. Where the repository is thin is in setup and in grading. It links to the on-campus grading system for students at YSDA, and it says the course admin handles on-campus students. Off-campus readers get the notebooks and the issue tracker, and nothing else. That is a reasonable trade for a free MIT-licensed set of materials, but it should be stated rather than discovered. ## How it compares with Hugging Face's NLP course The closest widely used alternative is the Hugging Face NLP course, which is also free and also notebook-oriented. The difference is in what the materials are built around. Hugging Face's course teaches through the transformers library and its ecosystem, so the exercises and the API you learn are the same thing. This repository teaches the mechanism first: week 1 covers count-based methods, Word2Vec and GloVe before any pretrained transformer appears, and week 2 has you write an n-gram language model by hand. That shows up in the homework. Fine-tuning a pretrained BERT is week 4 here, after attention has been derived in week 3. In a library-centred course, that ordering is usually inverted: you fine-tune a model in the first lesson and learn the architecture later, if at all. Neither is wrong. If your goal is to ship something with transformers this month, the library-centred path gets you there faster. If your goal is to understand why attention heads do what they do, the sequence here is the more useful one, and week 3 explicitly covers probing for linguistic structure and the functions of attention heads. The practical consequence is that this course makes you write more code that no library would make you write. That is the cost and the benefit in the same sentence. ## Licence, maintenance and what an upgrade costs you The repository is MIT licensed, which is permissive: you can reuse, modify and redistribute the materials, including commercially, provided the licence and copyright notice are preserved. The LICENSE file sits at the top level. This is not legal advice, and if you plan to build teaching material on top of the notebooks, read the licence text yourself rather than a summary of it. The last push to the repository was on 2026-09-18, so the project is being updated rather than abandoned. There are no retrieved releases, which is consistent with the shape of the project: it is a set of course folders, not a versioned package, so there is nothing to pin and no changelog to read. Upgrading means pulling the branch and re-reading the week READMEs, because the README states that materials are added as they are prepared and that the current iteration is 2025. The real upgrade cost is your environment. Since dependencies are discussed in an issue thread rather than declared in the repository, a pull that brings in a new week can also bring in a library version you do not have. Budget for that: after pulling, check the week README and the troubleshooting thread before assuming a notebook will run. ## Conclusion Adopt it if you want a structured, notebook-first sequence that runs from word embeddings to agents and you are willing to set up the environment yourself, since the README points to a single issue thread rather than a pinned requirements file. Do not adopt it if you need graded certificates, scheduled deadlines, or a course that answers the question of what an NLP course costs, because the repository is materials, not a service, and it publishes no fees, no schedule and no credential. Before you start, open week01_embeddings/README.md and read the install-and-troubleshooting thread linked from the top-level README, then confirm that the notebooks for the week you want are actually present in the branch you cloned. ## FAQ ### What is the yandexdataschool/nlp_course about? It is the YSDA Natural Language Processing course, published as weekly folders of lecture and seminar materials. The syllabus runs from word embeddings and language modeling through attention, transfer learning, large language models, prompting, fine-tuning, efficiency, retrieval-augmented generation, agents and interpretability. ### How long is the yandexdataschool/nlp_course? The syllabus lists fourteen weeks, from week01_embeddings through week14_agents_production. The README does not give a calendar, and it states that materials are added as they are prepared, so the listed weeks are the plan rather than a schedule. ### Can I take the yandexdataschool/nlp_course online for free? The materials are on GitHub under the MIT licence and the lecture material links to interactive pages on lena-voita.github.io, so the reading and notebooks are openly available. The README does not describe enrolment, fees or certificates for off-campus readers. ## Sources - [Issues](https://github.com/yandexdataschool/nlp_course/issues) - [License: MIT](https://github.com/yandexdataschool/nlp_course/blob/2026/LICENSE) - [Project website](https://lena-voita.github.io/nlp_course.html) - [README](https://github.com/yandexdataschool/nlp_course/blob/2026/README.md) - [yandexdataschool/nlp_course on GitHub](https://github.com/yandexdataschool/nlp_course) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/yandexdataschool-nlp-course