machine-learning-interview VS coding-interview-university

The coding rounds are usually typical Leetcode fare, but the domain round could be anything. It's usually "how would you design a ML system that would do X" where X is something that the company would care about. I would browse this resource to help you: https://github.com/khangich/machine-learning-interview

Minimum Viable Study Plan for ML interviews

I'm doing MLE interviews right now. I was looking at this https://github.com/khangich/machine-learning-interview.

And https://github.com/khangich/machine-learning-interview

✅ jwasham/coding-interview-university : https://github.com/jwasham/coding-interview-university

4) Coding Interview University: Conquer the dreaded coding interview with this battle-tested arsenal of algorithms, data structures, and interview prep strategies. Sharpen your problem-solving skills, optimize your coding efficiency, and ace those technical assessments with this invaluable resource. (https://github.com/jwasham/coding-interview-university)

View on GitHub

I stumbled upon a github post called coding interview university and with the shiny object syndrome I have, I kinda want to ditch the roadmaps and start something new like this one in github, or the random dude I found in YouTube, telling his audience that he passed the FAANG interviews. The annoying part of me is that whenever I hear stuff like “I passed the FAANG”, I will immediately try to follow their path because their method works for them or to some other people