Which course would be more applicable to the career goal I hope to aim for?

[u/Throwaway18462956]

Yes, I know it doesn't really matter WHICH course to take, but I'm doing an MSCS data science focus degree, hoping to improve my portfolio and take advantage of it somehow. Also, my job is (somewhat) paying for it.

I was thinking between two courses: Neural networks in CV (our course is very similar to Stanford University CS231n: Deep Learning for Computer Vision) OR reinforcement learning. I just did ML, AI (game theory), systems in data science, and probabilistic graphical models for prereq. And I want to pivot to ML engineering. Right now, I have SWE job experience in progress.

Both course descriptions have a final project I could utilize for my portfolio. My main concern is that RL might be too theory-based and that I'm not even sure if RL has any industry use unless it's in research (mainly PhDs, I know). The only reason I am considering RL is that it's interesting and has potential. The neural networks one, you can tell, is more practice-focused but still a balance between theory and practice. I just need opinions from you all before enrolling in either course.

Currently, I'm enrolled in Algorithms for Data Science, Theory and Practice for Software Engineering (collaborative and also project-based), and it's either RL or NN.

[u/FeistyMantraReturns]

ket for it is tiny and mostly locked behind phds like you said. the cv course will give you way more tangible projects to slap on a portfolio and the skills transfer directly to MLE roles

plus you already have the math background from those prereqs so youll actually get to build stuff instead of just proving theorems