machine-learning-interview VS interview

Machine learning interview

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

Though people will argue that there are many free resources to learn dsa and then hop to leetcode , I would always advise people who can afford and can't figure out where to start to go for any dsa paid course which is popular. It gives you a structured path and clears the blurriness of one's journey. Though if you want a free structured path - https://github.com/Olshansk/interview . Follow this .

https://github.com/donnemartin/system-design-primer https://github.com/Olshansk/interview https://github.com/DopplerHQ/awesome-interview-questions https://github.com/jwasham/coding-interview-university https://github.com/yangshun/tech-interview-handbook

A troubling trend DistantJob has noticed is how developers prepare to ace interviews – even if their skills and talent don’t extend to much else beyond that prep. Books, website, interview problems are all at the disposal of a developer. Acing an interview is no guarantee of acing the actual job.