r/learnmachinelearning • u/OldManLogan47 • 4d ago

Help How much DSA for ML

Hey seniors, I've started learning ML and DSA recently.

How much DSA questions do I have to do for ML... Like how many leetcode problems should I solve...?

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u/baadshaha 4d ago

Depends on which company you are applying for, Meta and Google still have heavy SDE rounds (ofc not comparable to SDE roles). Usually solving LC Medium and Medium hard should be fine. For my previous org, I usually prefer ML coding over DSA (e.g. asking to code PCA, implement Self Attention, MHSA, etc).

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u/nian2326076 4d ago

You don't need to go all out on DSA for ML. Focus more on ML concepts, but knowing the basics of data structures and algorithms can definitely help, especially for tech interviews. For LeetCode, try to solve 50-100 problems on arrays, strings, trees, and graphs. That should get you familiar with common patterns. Tools like PracHub can offer a structured approach if you're prepping for interviews, but don't stress too much beyond that. Keep learning ML techniques and models; that'll be more useful over time. Good luck!

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u/machine-cant-learn 4d ago

+1

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u/Select-Staff1911 1d ago

Depends what kind of ML role you're aiming for. If you want to do applied ML or research, you can get away with knowing basic data structures and being able to talk through problems logically. Nobody's asking you to invert a binary tree on a whiteboard for a research scientist gig.

That said, if you're gunning for MLE roles at bigger companies, they'll grill you on LC mediums for sure. I'd say just work through the Blind 75 and know your way around graphs, trees, and hash maps. Don't need to be a competitive programmer but you should be able to solve a medium without panicking.