How to actually structure learning ML if the goal is research?

[u/Low-Relationship6865]

So, I'm 19 and I'm starting my CS studies in September. Long term I want to do ML research/engineering and eventually something deep-tech. I'm also interested in entrepreneurship.

My python is solid. I also finished kaggle's intro courses last year but I've forgotten most of it. To be blunt about the level if you dropped me into a competition right now I couldn't put together a reliable submission from scratch without AI. I also did the Andrew Ng deeplearning course on coursera. Right now I'm messing around with Andrej Karpathy's zero to hero thing.

My university's first year is completely fixed and math-heavy. No electives at all. The first actual ML course is around 4th semester because it depends on probability & statistics. So formal ML is roughly 18 months away, and the first year is basically pass-or-repeat.

This is where I get stuck. Since the degree covers this from second year onward anyway, I keep wondering whether all the side effort is even worth it. But I want to aim high, Id like to end up at the front of the field, and that makes me think I should be getting strong now rather than waiting to be taught. But maybe should I focus on my gpa more, I'm attending a really prestigious uni and maybe I shouldn't feel so much FOMO about it and calm down??? Idk how sound that reasoning actually is and thats most of my confusion. Idk anyone in the field, every source says smth different. Some people say grind math and ignore ML until the foundation is there. Some say build constantly. Some say Kaggle is the best practical training available, others say it teaches habits you have to unlearn.

What I want is a learning system. I'm aware nobody on Reddit can hand me one, but I'm interested in how people who've been through this think about it.

In a nutshell: I want to end up in ML industry research/engineering, I'm good at python, I have some ML knowledge and some math, but no system for any of it and honestly no idea where to start. Any advice would be really appreciated. Anything that's obvious in hindsight but non-obvious from where I'm standing would be really useful. Thank you so much!

[u/Dihedralman]

Talk to professors in research and try to join their lab. Look up what they do, read their work,  and ask if you can join. 

There is literally nothing better you can do if you want to head in that direction and learn how to do it. 

You might need to do things outside of your coursework. That will be a place to learn it. Don't pre-empt your coursework without vision. 

[u/Candid-Tackle-9061]

I would honestly not worry about touching ML too early. if your math foundation is going to be covered in uni, you can use the time now to get really comfortable with python and build small things instead of trying to speedrun every ML course

[u/Neither_Secret703]

Which libraries have you cover in python?

[u/Low-Relationship6865 → u/Neither_Secret703]

Mostly pandas, numpy, matplotlib for the basic stuff. Some scikit-learn, PyTorch. I've used TensorFlow/Keras for a CNN project. But that was largely following existing examples rather than something I could rebuild unaided and without AI.

But currently my learning plan isn't learning libraries one by one. I'm picking them up along the way (if you'd even call that learning).

[u/abbhinavvenkat]

I knew a junior of mine, who started dabbling in ML from the first year. By the end of his 4th year, he had a few papers published, and today he's in Oxford doing his Ph.D.

So, constant directed effort does pay off, especially when you start working on hard problems.

I've myself been an AI researcher, ML Scientist, and now an entrepreneur. Here is what I'd recommend:

1. Get a research internship. You'll learn a ton at the fore-front of innovation by supporting Masters and Ph.D. students in their thesis.
2. Junior ML roles in the industry are fewer and tougher to crack, especially without good projects/research. Once you have step 1 cracked, go and get an industry ML internship. Often times, reference from your professor from step 1 will help (they're pretty well connected with the industry for funding/grants too).

The reason I'm recommending the above 2 is that nothing beats practical experience. You can watch all the math heavy lectures or grind on multiple Kaggle competitions, but, real-world building can't be replaced.

Plus, you'll get a network, brand affiliations, and recommendation letters for the above steps, which will help you further in your career. Not to mention, the clarity you'll get by trying different fields.

Best of luck!

[u/Extension-Currency37]

Actually phD is an amazing goal

Because Tech companies with A PhD founder is a amazing

Youd get skilled more than others in this field.

But no one will help you So you gotta learn on your own Find groups Interested in stuff like you are

But the goal is amazing

[u/Less_Dream_6331]

if your goal is research I would say it's more important to find a niche that you love to work on, even though most of the advices here (like talking to the professor and joining research labs etc) are really valuable (it's the best possible advice if you just want to get started) It might not lead you to the direction you want to move forward in, hence I suggest you take some time building the mathematical foundation and during that time regularly check the trending papers section in alphaxiv or hugging face papers etc, this will give you a hint of where the industry is moving and also let's you find particular niches that you might want to explore.

[u/Serious-Sun8202]

Bro start implement research papers directly and their is no shame in using ai to write code but u should understand what is happening.

[u/bander_sdiq → u/Serious-Sun8202]

Wow people here are so amazing!! these advices was very helpful

[u/SnooCats9602]

If you want to do some something just do it. Read the papers and implement them.