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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
:memo: Подборка ресурсов по машинному обучению
| Date | Stars |
|---|---|
| 2026-07-24 | 1454 |
| 2026-07-25 | 1455 |
| 2026-07-28 | 1455 |
| 2026-07-30 | 1455 |
| 2026-08-06 | 1455 |
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# Машинное обучение Постоянно обновляемая подборка ресурсов по машинному обучению. #### Оглавление * [Библиотека ML-специалиста](#Библиотека-ml-специалиста) + выбор редакции: * [Дополнительные материалы](https://gist.github.com/demidovakatya/cef3d462bcf56b84f56950ea490a9e8e) к курсу «Введение в машинное обучение» * [Рекомендации](https://gist.github.com/demidovakatya/61e15717a9eefae0bd237b7fd959d166) от преподавателей специализации «Машинное обучение и анализ данных» * [Литература для поступления в ШАД](https://gist.github.com/demidovakatya/873e4dd6f1c6652ac842) * [Подборка научпоп-книг](https://bookmate.com/bookshelves/Nggk0rBi) * По темам: * [Big Data](/big-data.md) * [Dataviz](/dataviz.md) * [LaTeX](/latex.md) * [NLP](/nlp.md) * [Python, IPython, Scikit-learn etc](/python.md) * [R](/r.md) * [Алгоритмы](/algorithms.md) * [Линейная алгебра](/linalg.md) * [Нейронные сети, Deep learning](/neural-nets.md) * [Статистика и теория вероятностей](/probability-statistics.md) * [Онлайн-курсы (MOOC)](#Онлайн-курсы-mooc) * [Чаты/паблики/каналы про ML](#social) ---------------------------------------------------- * [Календарь соревнований по анализу данных](https://mltrainings.ru/?filter=active) * [Машинное обучение: вводная лекция](http://www.machinelearning.ru/wiki/images/f/fc/Voron-ML-Intro-slides.pdf) – К. В. Воронцов * [Lecture notes and code for Machine Learning practical course on CMC MSU](https://github.com/esokolov/ml-course-msu) * [100+ Free Data Science Books](https://www.learndatasci.com/free-data-science-books/) – более 100 бесплатных книг по Data Science * [Free O'Reilly data science ebooks](https://www.oreilly.com/data/free/archive.html) * [100 репозиториев по машинному обучению](http://meta-guide.com/software-meta-guide/100-best-github-machine-learning) * [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning) — A curated list of awesome Machine Learning frameworks, libraries and software * [Open Source Society University's Data Science course](https://github.com/ossu/data-science) – this is a solid path for those of you who want to complete a Data Science course on your own time, for free, with courses from the best universities in the World * [Доска по data science в Trello](https://trello.com/b/rbpEfMld/data-science) — проверенные материалы, организованные по темам (expertise tracks, языки программирования, различные инструменты) * [Machine Learning Resource Guide](https://www.pdf-archive.com/2017/09/02/machine-learning-resource-guide/machine-learning-resource-guide.pdf) * [17 ресурсов по машинному обучению от Типичного Программиста](https://tproger.ru/articles/free-programming-books/#machine-learning) * [51 toy data problem in Data Science](https://www.quora.com/Data-Science/What-are-some-good-toy-problems-can-be-done-over-a-weekend-by-a-single-coder-in-data-science-Im-studying-machine-learning-and-statistics-and-looking-for-something-socially-relevant-using-publicly-available-datasets-APIs/answer/Alex-Kamil) * [practical-pandas-projects](https://github.com/schlende/practical-pandas-projects) — project ideas for improving one's Python data analysis skills * [Dive into Machine Learning](https://hangtwenty.github.io/dive-into-machine-learning/) * :octocat: [Dive into Machine Learning repo on github](https://github.com/hangtwenty/dive-into-machine-learning) * [Data Science Interview Questions](https://www.itshared.org/2015/10/data-science-interview-questions.html) — огромный список вопросов для подготовки к интервью на позицию data scientist'а * [Много книг по Natural Language Processing](https://www.dropbox.com/sh/b1c2ulwua9zy574/AACswS1E0IB9LdPDxQ6fexm4a?dl=0) * [Список открытых источников данных, на которых можно найти бесплатные датасеты](/datasets.md) * [What should I learn in data science in 100 hours?](https://www.quora.com/What-should-I-learn-in-data-science-in-100-hours-I-am-free-for-the-next-10-days-and-would-like-to-learn-whatever-I-can-in-the-n
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fc46e9558ab0f910, topic:nlp, readme:natural language processing
matched fp:fc46e9558ab0f910, topic:deep-learning