|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Awesome Scalability||48,572||a day ago||15||mit|
|The Patterns of Scalable, Reliable, and Performant Large-Scale Systems|
|C||16,956||16 hours ago||37||gpl-3.0||C|
|Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.|
|Interview||8,171||2 months ago||1||other||Jupyter Notebook|
|Interview = 简历指南 + 算法题 + 八股文 + 源码分析|
|Data Science Interviews||7,788||5 days ago||11||cc-by-4.0||HTML|
|Data science interview questions and answers|
|Machine Learning Interview||6,879||a month ago||3|
|Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.|
|Deep Learning Interview Book||5,149||7 months ago||9|
|Awesome Ai Residency||2,527||5 months ago|
|List of AI Residency Programs|
|Machine Learning Interviews||2,220||7 days ago||2||mit||Jupyter Notebook|
|This repo is meant to serve as a guide for Machine Learning/AI technical interviews.|
|Data Science Interview Resources||1,926||a month ago||8||mit|
|A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.|
|Awesome Interview||1,562||3 years ago||1||other|
|Collection of awesome interview references.|
Machine Learning System Design - Early Preview - Buy on Amazon
Follow News about AI projects
|1. Youtube Recommendation|
|2. The main components in MLSD|
|3. LinkedIn Feed Ranking|
|4. Ad Click Prediction|
|5. Estimate Delivery time|
|6. Airbnb Search ranking|
|List of promising companies||WealthFront 2021 list.|
|Prepare for interview||Common questions about Machine Learning Interview process.|
|Study guide||Study guide contained minimum set of focus area to aces your interview.|
|Design ML system||ML system design includes actual ML system design usecases.|
|ML usecases||ML usecases from top companies|
|Test your ML knowledge||Machine Learning quiz are designed based on actual interview questions from dozen of big companies.|
|One week before onsite interview||Read one week check list|
|How to get offer?||Read success stories|
|FAANG companies actual MLE interviews||Read interview stories|
|Practice coding||Leetcode questions by categories for MLE|
|Advance topics||Read advance topics|
NOTE: there are a lot of companies that do NOT ask leetcode questions. There are many paths to become an MLE, you can create your own path if you feel like leetcoding is a waste of time.
I use LC time tracking to keep track of how many times I solves a question and how long I spent each time. Once I finish non-trivial medium LC questions 3 times, I have absolutely no issues solving them in actual interviews (sometimes within 8-10 minutes). It makes a big difference. A better way is to use LeetPlug chrome extension here
I really found the quizzes very helpful for testing my ML understanding. Also, the resources shared helped me a lot for revising concepts for my interview preparation. This course will definitely help engineers crack Machine Learning Engineering and Data Science interviews.
I really like what you've built, it'll help a lot of engineers.
I have been using your github repo to prep for my interviews and got an offer with NVIDIA with their data science team. Thanks again for your help!
Woow this is very useful summaries, so nice.
The repo is extremely cohesive! Thanks again.
This repo is written based on REAL interview questions from big companies and the study materials are based on legit experts i.e Andrew Ng, Yoshua Bengio etc.
I have 6 YOE in Machine Learning and have interviewed more than dozen big companies. This is the minimum viable study plan that covers all actual interview questions from Facebook, Amazon, Apple, Google, MS, SnapChat, Linkedin etc.
If you're interested to learn more about paid ML system design course, click here. This course will provide 6-7 practical usecases with proven solutions. After this course you will be able to solve new problem with systematic approach.
If you find this helpful, you can Sponsor this project. It's cool if you don't.
Thanks to this community, we have donated about $200 to HopeForPaws. If you want to support, you can contribute too on their website.