I learned some basic ML from Andrew Ng's Coursera course more than 10 years ago, recently I graduated from the Math Master program and have some free time in my hand, so I am thinking about picking up ML/DL again.
In [Yacine's video](https://www.youtube.com/watch?v=ph6PIchDOcQ), he mentioned [fast.ai's course](https://course.fast.ai/), which I heard of in the past but didn't look into too much. The table of contents of [the book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) looks pretty solid, but it was published in 2020, so I was wondering given the pace of AI development, do you think this book or course series is still a good choice and relevant for today's learners?
*To provide more context about me*: I did math major and CS minor (with Python background) during undergrad but have never taken any ML/DL courses (other than that Coursera one), and I just finished the Master program in math, though I have background and always have interests in graph theory, combinatorics, and theoretical computer science.
I have two books "Hands-on Machine Learning" by Geron and "Hands-on LLMs" by Alammar and Grootendorst, and plan to finish Stanford's CS224N and CS336 and CMU's DL systems when I have enough background knowledges. I am interested in building and improving intelligent systems such as DeepProver and AlphaProof that can be used to improve math proof/research.
Thank you a lot!