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
Yeah, I also started with Andrew Ng's lectures, they're really good.
But CS231n is best taken with PyTorch instead of TensorFlow... don't you think? And adding a few projects to your portfolio really helps.
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.