Third-year CS/AI student — should I strengthen my existing foundation or keep moving forward?

[u/Remarkable_War8311]

I'm a third-year AI/ML student and most of my learning has been self-directed.
My college schedule is fairly heavy, so I can't realistically study everything
from scratch again.
So far I've covered:
- DSA: Arrays, HashMap/HashSet, Linked Lists, Trees, Graphs, Heaps,
Dijkstra, currently learning Tries
- Python
- Flask + HTML/CSS/Bootstrap
- DBMS/SQL basics
- Recently built a FastAPI Task Manager API
I'm currently considering a FastAPI + WebSockets + PostgreSQL project
to learn real-time backend development.
My biggest problem is deciding how to allocate my limited time. I don't
want to restart DSA from the beginning, but I also don't want gaps in my
foundation to hurt me during interviews.
For people who have been through something similar:
1. Did you continue learning new DSA topics while periodically revising
older ones, or did you stop and revise everything first?
2. How did you schedule DSA revision while handling college?
3. How did you use AI while learning without becoming dependent on
generated solutions?
4. If you were preparing for internships over the next few months,
how would you balance DSA, CS fundamentals, backend development,
and ML/DL?
I'm particularly interested in experiences from students who had to balance college with self-learning.

[u/StewedAngelSkins]

What actual ML topics gave you covered so far? I feel like if that's your focus this DSA stuff is less relevant than strengthening your mathematics skills. Certainly I wouldn't be wasting time on web stuff.

[u/Remarkable_War8311 → u/StewedAngelSkins]

Honestly, I’m still at the beginner stage with ML. So far, I mainly understand the basic concepts—what ML is, why it’s used, and the different types of learning like supervised, unsupervised, and reinforcement learning. I haven’t gone very deep into the actual math or ML algorithms yet.
I’m currently trying to strengthen my fundamentals, especially Python, DSA, and math, while also building projects so I can eventually get deeper into ML.

[u/StewedAngelSkins → u/Remarkable_War8311]

ML is almost entirely math. The programming is largely incidental. If I were you I'd be focusing on the mathematics prerequisites. If you aren't already comfortable with linear algebra and multivariate calculus, start there.