ashishpatel26's 500+ AI project list is a three column link table, not a code collection
500 AI Machine learning Deep learning Computer vision NLP Projects with code
At a glance
- What is it?
- The repository is a README whose rows point at other people's GitHub repos and Medium articles, alongside an images folder and a .github folder. It works as an index of project names, claims 500+ entries, records no license, and ships no code, so every triage decision lands after the click.
- Who is it for?
- Use it as an index when you want breadth of machine learning, computer vision, and NLP project names, and do not treat it as something you can build from or ship. It ships no code, no license, and no difficulty signal, so open the destination and check its own license before relying on any row.
- Can I use it commercially?
- Not without permission. GitHub finds no licence file in the repository, and without a licence all rights are reserved by default: you may read the code but not reuse it. Check the README, or ask the authors, before using it.
- Is it still maintained?
- Yes. The repository last received commits 5 days ago.
- What is it written in?
- GitHub does not report a main language for this repository.
Answers come from the project's GitHub data, last synced on October 1, 2026, and from our analysis. They are not legal advice.
Editorial analysis
Three columns in a README and no other file to run
The whole repository is a link table. Its top level holds three entries: .github/, README.md, and images/. There is no package manifest, no requirements file, no notebook, no test, and no license file, so there is nothing to install, import, or pin. Reading the repository is the entire usage model: open README.md, scan the serial number, the name, and the link, then click the destination. That makes the project a pointer rather than a tool, which is worth settling before you plan time around it. A reader who wants a sentiment analysis walkthrough gets a row pointing at amankharwal.medium.com/6-sentiment-analysis-projects-with-python-1fdd3d43d90f, and from that point the actual code, its environment, and its failure modes live somewhere else entirely. Nothing in this repository holds that content, so none of it can be reviewed, versioned, or fixed from here.
Every row ends in a bare pointing hand glyph, so the table reads as a wall of identical cells
The name column carries the text, and the link column carries a single pointing hand glyph followed by the destination in parentheses. The reader gets hundreds of identical link bodies to interpret, and the link itself is not a label, so copying it from the rendered page yields a URL with no title attached to it. In raw Markdown every row has the same shape: a serial number, a name, a glyph, a URL. All the value sits in the name column, which means the signal you act on is the author's phrasing rather than the target itself. That phrasing is descriptive, not prescriptive. Rows read like 6 Sentimental Analysis Projects with python, 23 Iot Projects with Source Code for 2021, and 50 + Code ML Models (For iOS 11) Projects, spelling and parenthetical qualifiers included. The qualifier tells you the target platform in one case. Nothing in the row states difficulty, runtime, dataset size, or whether the code still runs against the library versions you have installed.
Rows split between GitHub repos and Medium posts, and the Medium half is the fragile half
The table mixes two very different kinds of destination. Rows 1, 2, 3, 33, 34, 36, 37, 38, 39, and 40 point at GitHub repositories: ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post, ashishpatel26/Treasure-of-Transformers, ashishpatel26/Andrew-NG-Notes, aymericdamien/TopDeepLearning, josephmisiti/awesome-machine-learning, jbhuang0604/awesome-computer-vision, ashishpatel26/Real-time-ML-Project, vinta/awesome-python, fighting41love/funNLP, and likedan/Awesome-CoreML-Models. Rows 4, 9, 13, 16, and 19 sit on Medium instead. The rest land on thecleverprogrammer.com, data-flair.training, kdnuggets.com, becominghuman.ai, and medium.datadriveninvestor.com. A GitHub row can be cloned, forked, or pinned to a commit. A Medium row gives you prose with a publication date and a host you do not control. Consequence: if your plan is to keep a working reference implementation, only the GitHub half of this list supports it, and the split is invisible until you start clicking.
Two rows, one destination, and a title number you cannot check
Row 24 is 47 Machine Learning Projects for 2021 and row 26 is 28 Machine learning Projects for 2021, and both resolve to the identical address data-flair.training/blogs/machine-learning-project-ideas/. The table counts them separately, so the total in the title is a count of rows rather than a count of distinct places to read. That distinction matters when you budget an evening: two visible rows buy you one article. The same arithmetic runs inside the destinations. Row 33 is 500 + Top Deep learning Codes, row 34 is 500 + Machine learning Codes, row 36 is 1000+ Computer vision codes, row 38 is 1000 + Python Project Codes, and row 39 is 363 + NLP Project with Code. Those figures belong to the linked lists, not to this repository, and the file never reconciles its own total against them. Consequence: a headcount taken from the serial number column overstates how much distinct material you can actually reach, and the 500+ in the name is not a number the file lets you verify.
Nine rows are stamped for 2021 and several addresses carry 2020 dates
Nine of the visible rows put a year in the name: 47 Machine Learning Projects for 2021, 19 Artificial Intelligence Projects for 2021, 28 Machine learning Projects for 2021, 16 Data Science Projects with Source Code for 2021, 23 Deep learning Projects with Source Code for 2021, 25 Computer Vision Projects with Source Code for 2021, 23 Iot Projects with Source Code for 2021, 27 Django Projects with Source Code for 2021, and 37 Python Fun Projects with Code for 2021. All nine land on data-flair.training. Other addresses carry earlier dates inside the path itself, for example thecleverprogrammer.com/2020/11/22/deep-learning-projects-with-python/ and the course post at thecleverprogrammer.com/2020/09/24/machine-learning-course/. Consequence: a list whose newest visible tier is labelled 2021 is a reading list, not a current map of the field, and a 2020 article will not tell you what changed in the frameworks it uses, so repairing the environment becomes your first task rather than theirs.
No license, no language, and no difficulty column, so reuse decisions happen after the click
Three things a reader needs before clicking are missing. No license is recorded for this repository and no license file sits at the top level, so you have no terms to check before reusing anything you reach through it. Primary language is not recorded either, which tells you nothing about what the linked material expects you to have. And the table has no column for difficulty, runtime, dataset, or hardware, so there is no way to narrow hundreds of rows down to the two that fit the time you actually have. Consequence: every selection you make from this list is made after the click rather than before it. If you need code you can ship, this table cannot tell you which destinations qualify. You would open each link, read its own license, and judge it on its own, which is the filtering work an index was supposed to remove.
The only stated update path is a pull request, and no release pins anything
The file states three things about upkeep: the list is continuously updated, you can take pull requests and contribute, and all links are tested and working fine, with a request to ping if a link does not work. The repository is not archived, and its last push is dated 2026-09-26, so the table is being touched rather than frozen. It has no GitHub releases, so there is no tag to pin and no version to record in a reading list, and the default branch is the only identifier available. Consequence: a hash from today and one from last year both read as main, so any note you keep that cites this list by branch will drift out of date without warning. If the link guarantee matters to your work, capture the commit you read and check the destinations yourself, because the ping request hands the verification back to the reader.
Editorial conclusion
Use it as an index when you want breadth of machine learning, computer vision, and NLP project names, and do not treat it as something you can build from or ship. It ships no code, no license, and no difficulty signal, so open the destination and check its own license before relying on any row.
Frequently asked questions
What are some of Ashishpatel26's 500+ AI Agent projects?
The rows that point back to the same author are 365 Days Computer Vision Learning, 125+ NLP Language Models Treasure of Transformers, Andrew NG ML notes, and 300 + Industry wise Real world projects with code. None of the visible rows is labelled an AI agent project, so the list does not answer that question in its current form.
What are the top 10 machine learning projects?
The table carries no ranking by quality, difficulty, or popularity. The serial number column runs consecutively through topics, with row 1 a computer vision learning series, row 2 an NLP transformer list, row 3 Andrew NG ML notes, and row 4 a time series forecasting article.
Which AI is best for ML projects?
The visible rows name collections of projects rather than models or tools, and the file records no prerequisites, so it cannot settle that choice. It also records no license, so nothing reached through it carries terms you can check here.
What are some good Python AI projects with source code?
Rows include 5 Web Scraping Projects with Python, 7 Python Gui project, 30 Python Project Solved and Explained, 20 Deep Learning Projects Solved and Explained with Python, and 50 + Code ML Models (For iOS 11) Projects. Whether any is good for reuse is not stated, since no license is recorded for this repository or its destinations.