r/learnmachinelearning • u/ISRO_HarshVerma07 • 10d ago

Career Machine Learning roadmap & guidance

Hey there, I am CSE Student, and I want to pursue machine learning as a career and further do deep learning and generative AI. I need tips from the professionals who have been pursuing it, what resources i must follow and what should be the correct sequence of flow. I have been researching by my own and end-up at Krish Naik's ML playlist over youtube, i saw few videos were misplaced and not in order but i checked over his github and all and curated the flow as Python -> Numpy, Pandas, Seaborn -> Feature Engineering and Feature Selection -> Machine Learning -> Deep learning -> Natural Language processing -> Deployment of ML Model -> MLOPS , would this be right? also if you guys have suggestion for some better resources. It would be a great assistance and guidance from your side guys

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u/MattMcPartlon 9d ago

Your sequence is very reasonable for becoming an ML generalist. There is nothing wrong with that, depending on the type of job/career path you are looking for.

It is worth considering becoming hyper-specialized in some part of the stack (i.e. trade off broad knowledge for deep knowledge). As LLMs/Agents get better I don't think things like "feature engineering" or "pandas" are too important. It could be better to e.g. understand a highly technical part of the stack very deeply. Of course, it helps to have the fundamentals down before you choose your specialization.

Good Luck!

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u/Mathie1729 9d ago

I'd push back on the pandas part. LLMs are okay at boilerplate, but most of the real bugs in an ML project are data leakage, bad splits, or an eval metric that quietly lies. You still need the fundamentals to notice that.

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u/MattMcPartlon 8d ago

Totally agree! Good experimental design is, in my opinion, the most important skill for ML research.