Home Projects MLInterview
MLInterview
ai-careers

MLInterview

:octocat: A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!

by theainerd · GitHub
Stars
Forks
License
Created
Last commit
Category
ai-careersawesome-listdata-scienceMIT
View on GitHub
In plain words

Prepare for data science job interviews with a collection of resources and tips tailored for AI careers in India.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
MLInterview — GitHub preview card
📈 Star history
545544
2026-07-202026-08-31
📈 Track MLInterview

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

:octocat: A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!

MLInterview has 544 stars on GitHub. It has been forked 172 times. It has been in active development since 2018. MLInterview is available under the MIT license. Its main topics are ai-careers, awesome-list, data-science, data-science-interview.

Frequently asked questions

What is MLInterview?

:octocat: A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!

Is MLInterview open source?

MLInterview is an open-source project. It is released under the MIT license.

Is MLInterview free?

Yes. MLInterview is free and open source — you can use, modify and self-host it.

What license does MLInterview use?

MLInterview is available under the MIT license.

🏅 Maintainer of this project?
olud.ai badge — MLInterview

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=theainerd-mlinterview)](https://olud.ai/project/theainerd-mlinterview.html)
More badge options →
🧬 Shares DNA with

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.