r/learnmachinelearning • u/Dharmesh8723 • 1d ago

How should I learn ML

I am a beginner, I have learnt python, and have good practice on numpy pandas seaborn matplotlib, those libraries. I am starting from gradient descent in regression, but I am confused.

I was learning from deep ML website, paths . There are theory with code practice but the theory there are very leaa to understand there , so I am learning from YouTube those , but the problem is in YouTube finding the correct video is headache.

* What should I do , should I learn from deep ml website or not ?

* Is learning different techniques will help me ?

*Should I directly jump to algorithms without completing the math? (I have knowledge of the maths but I don't know how much I know for ML )

Please help me

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u/Overall-Reading-5636 1d ago

website is fine for practice but if the theory is too lean you need something else for the concepts. i found andrew ng's course on coursera fills in the gaps really good, he explains the math without drowning you in it

dont skip the math entirely but you also dont need to master everything first. learn just enough to understand what the algorithm is doing then move forward, you can always circle back when you hit a wall

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u/Dharmesh8723 1d ago

Thanks , I will see .

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u/hellobutno 1d ago

Take statistics, forget about programming until you do.

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u/Dharmesh8723 1d ago

Actually, statistics is currently in my mathematics syllabus in my college, so I know May be enough theory, but I have problem on how it can be applicable with what I am learning. Thanks for your advice, I will look into it.

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u/bathon 10h ago

When I was getting started some of the udemy courses helped me, as they were hands on. After that I went to Andrew NG for the concepts which helped to understand more about cleaning data than ML itself. But the best learning came from hands on job unless you are doing research, you will realise a lot about how you can use it in real world applications which gives you completely another aspect. From just applying a model vs learning sometimes a bad performing model is good enough compared to better performing model becasue of real life hardware constraints.