Pursuing an AI Master’s Path with Coursera (Non-CS Background)
I'm a non-CS major transitioning to AI, studying through a combination of Coursera and graduate-level lectures.
Courses I've taken:
1. Andrew Ng's Machine Learning Specialization — solid foundation.
2. Deep Learning Specialization (Andrew Ng) — theory + practice, highly recommended.
3. Stanford CS231n (free on YouTube) — essential if you want deep depth in computer vision.
Pros:
- Much cheaper than tuition (about $40 per course?)
- Flexible schedule, can work full-time
- Assignments are at actual research level
Cons:
- Hard to stay motivated (self-study)
- No networking opportunities
- Not a degree, so may be less impressive for jobs
Still, the portfolio + certification combo got me into a startup. It definitely helps.
3 answers
If you're a non-major with a weak math background, those lectures alone might hit a limit. I did linear algebra and statistics first, so it was much easier. Still, if you keep at it, you can go anywhere lol.
But CS231n is best taken with PyTorch instead of TensorFlow... don't you think? And adding a few projects to your portfolio really helps.
Yeah, I also started with Andrew Ng's lectures, they're really good.