# ai_all_resources is sixteen topic folders and one README of course links, with no license attached > nivu/ai_all_resources collects machine learning courses, playlists and community links, organised in the README by teaching level and in the tree by research area. It has no license file, its primary language reads as Jupyter Notebook, and the last visible section of the README stops inside a link. **nivu/ai_all_resources** — A curated list of Best Artificial Intelligence Resources - Repository: https://github.com/nivu/ai_all_resources - Stars: 1,536 · Forks: 314 - Language: Jupyter Notebook - License: not declared - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/nivu-ai-all-resources ## The description says one list, the tree holds sixteen folders The repository description is a curated list of best artificial intelligence resources, and the README title claims a compilation for mathematics, machine learning and deep learning. The checkout is neither shape. At the root there is the README, a `_config.yml`, and sixteen entries: four topic directories and twelve files or folders that read as a filing system rather than a list, among them autonomous_car, computer_vision, data_engineering, gans, generative_ai, reinforcement_learning, robotics, stat_prob, talks, webinars, new_resources, and two standalone markdown files named ai_job_concepts.md and tinyml.md. So there are two parallel organisations of the same material. The README sorts by what a learner needs next, moving from introductory courses to university programmes to playlists. The tree sorts by subject area, which is where the jobs, talks, webinars, tinyML and tweets material lives, and where a folder called new_resources holds whatever was added most recently. Nothing visible connects the two views, and no table maps a topic folder to a README section. One detection detail is worth stating plainly: the repository's primary language is recorded as Jupyter Notebook. The visible root has no notebook, so the notebooks live in the subdirectories, which makes this a mixed resource folder with runnable material inside it rather than a pure link collection. ## The community section covers two Meetup groups in one city The Communities to Follow section is short and geographically specific. AI Coimbatore appears with a Meetup page, a Telegram invite for daily updates and a Facebook group for Coimbatore School of AI. TensorFlow User Group Coimbatore appears next, with the abbreviation TFUGCbe, its own Meetup page and another Facebook group. Both are the same city and both are linked to the general TensorFlow community groups page. That tells you what the list was built around. A curriculum assembled in a working technology group, by someone who runs the group, points outward to Stanford, CMU, MIT and UCL for depth but inward to a local meetup for the people to actually ask questions in. The Telegram channel described as for daily updates is the same shape: a firehose of links from an active community rather than a curated reading queue. For a reader outside Coimbatore this is the least portable part of the file. A meetup room and a Telegram group are place-bound in a way a playlist is not, and neither carries a date, a frequency of meetings or a stated language of delivery, so there is nothing in the link to indicate whether either is still running. ## No LICENSE file and no license field, so reuse terms are unstated The license is recorded as unknown and there is no LICENSE file at the root of the tree. Nothing in the README addresses licensing either. That is the whole of the position, and it is worth stating precisely because a link list is the kind of project people fork without thinking. A repository of outbound links raises two separate questions. The first is the list itself, the curation, the arrangement of entries into sections and the commentary attached to some of them. The second is each linked resource, which stays under its own terms and is usually the more restrictive one, since several entries point at courses and books that other people publish. Neither question is answered here for the first one, and the file makes no attempt at the second. What this means in practice is that a fork is not obviously permitted. Nobody has asserted a public domain dedication, an open source grant or even a share-alike condition, so the absence of a license is not evidence that copying is fine. If you want this list inside a company wiki, inside a course handout or inside a slide deck, that is a question for the person named as maintainer rather than something to infer from the file. ## Eight Stanford playlists, and five of six CMU entries are one lecturer The Courses from Top Universities section is the densest and most specific part of the file. Stanford gets eight entries as YouTube playlists: CS221 on artificial intelligence from Percy Liang and Dorsa Sadigh, CS229 on machine learning from Andrew Ng, CS230 on deep learning from Andrew Ng, CS231n on convolutional networks from Fei-Fei Li and Andrej Karpathy, CS224n on natural language processing from Christopher Manning, CS234 on reinforcement learning from Emma Brunskill, CS330 on multi-task and meta learning from Chelsea Finn, and CS25 Transformers United. Five of the eight name Ng or Karpathy, so the list also serves as a pointer to particular instructors. Carnegie Mellon is organised by course number, LTI prefix, and reads as one person's teaching line: Graham Neubig appears for 11-711 on advanced NLP, 11-747 on neural networks for NLP, 11-737 on multilingual NLP and a low resource NLP bootcamp named for 2020, while 11-777 on multimodal machine learning goes to Louis-Philippe Morency and 11-785 on introduction to deep learning to Bhiksha Raj and Rita Singh. The link quality is uneven within that block. Most CMU entries point at a specific playlist, but 11-777 resolves to a channel videos page and 11-785 to a channel playlists page, so those two land on a listing page rather than a course sequence. MIT contributes three entries, 6.S191 with Alexander Amini and Ava Amini, 6.S094 with Lex Fridman and 6.S192 with Ali Jahanian, and University College London one, COMP M050 with David Silver. ## One section has a single product link, and the annotations are informal The section titled Anyone can do Machine Learning contains exactly one entry: Teachable Machine, described as training a computer to recognise your own images, sounds and poses without expertise or coding. It is the only commercial product in the visible part of the file, and it sits inside an otherwise academic list, under a heading that reads as a promise rather than a category. The commentary on individual entries is where the personal voice shows. The Stanford machine learning course on Coursera is annotated IMDB 10/10 LOL :P. vas3k's Machine Learning For Everyone carries a description that opens with a possessive typo, Summarize's, before explaining that it covers the algorithms and their applications in simple words with real-world examples. Udacity's Intro to Machine Learning gets a topic list running from Naive Bayes and SVM through PCA and evaluation metrics, and Intro to TensorFlow for Deep Learning is called the best course for learning TensorFlow, unqualified. Two of those Udacity entries point at classroom.udacity.com course pages for ud120 and ud187. Other entries carry explicit dates in their titles, most visibly the 2020 NLP bootcamp, so the file mixes undated links to course pages with dated ones and no mechanism for saying which of the undated ones still resolve to what they claim. The last section visible in the file is a Machine Learning Glossary entry whose link stops partway through the address, after the opening characters of the scheme. ## The creators get a name and a homepage, not a tutorial The file introduces itself as a compilation of tutorials created by the people it then links, and the list that follows is thirteen homepages and channels rather than tutorials. Karpathy's blog, Brandon Roher's e2eml school blog, Andrew Trask's site, Jay Alammar's site, Sebastian Ruder's site, Distill, Christopher Olah's notebooks, plus YouTube channels for StatQuest with Josh Starmer, sentdex, Lex Fridman, 3Blue1Brown, Alexander Amini and The Coding Train. Two entries are the same kind of thing twice over: Distill is a publication and The Coding Train and 3Blue1Brown are channels where the specific video is not named. So the unit of curation is a creator, not a piece. That is a defensible choice for a list meant to be revisited, since a good channel is durable in a way an individual lecture URL is not, and it is the reason the file has lasted at all. It is also why there is no way to answer what this repository recommends for a particular topic: the answer is a name, and picking from a name is the reader's work. The maintainer credit is just as spare. A heading reading This Repo is Created and Maintained by is followed by a name, Navaneeth Malingan, and two link tags pointing at an Instagram profile and a LinkedIn profile whose bodies are empty, so they render as nothing in a Markdown reader. There is no CONTRIBUTING file at the root, no stated way to submit a resource, and no criteria for what earns an entry. ## Conclusion Treat this repository as a bookmark file, not as a course. It is worth an hour of reading because the Stanford, CMU, MIT and UCL course blocks are specific and dated by name, but before you fork or mirror any of it, note that no license is stated in the metadata and no LICENSE file exists at the root, which means the terms for reuse are unstated rather than permissive. ## FAQ ### What does nivu/ai_all_resources actually contain? A README of links plus sixteen root entries: directories for autonomous_car, computer_vision, data_engineering, gans, generative_ai, reinforcement_learning, robotics, stat_prob, talks, webinars and new_resources, and standalone files ai_job_concepts.md and tinyml.md. There is no LICENSE file at the root. ### Who maintains nivu/ai_all_resources and who is it for? The README credits Navaneeth Malingan and links to an Instagram profile and a LinkedIn profile. The communities section points at AI Coimbatore and TensorFlow User Group Coimbatore, both with Meetup, Telegram and Facebook links. ### Which university courses does nivu/ai_all_resources list? Stanford CS221, CS229, CS230, CS231n, CS224n, CS234, CS330 and CS25; Carnegie Mellon LTI 11-711, 11-747, 11-737, 11-777 and 11-785 plus a low resource NLP bootcamp; MIT 6.S191, 6.S094 and 6.S192; and UCL COMP M050. All are YouTube playlists or channel pages. ### Can I fork or reuse nivu/ai_all_resources? No license is stated in the metadata and no LICENSE file exists at the root, so the terms are unstated rather than permissive. The linked courses belong to their own authors under their own terms, and the file does not address that either. ## Sources - [Issues](https://github.com/nivu/ai_all_resources/issues) - [nivu/ai_all_resources on GitHub](https://github.com/nivu/ai_all_resources) - [README](https://github.com/nivu/ai_all_resources/blob/master/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/nivu-ai-all-resources