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> Anyone is welcome to take this course, regardless of background. To get the most out of this course, we recommend that you: > Completed an introductory cours
by doctoboggan 4y ago
> Anyone is welcome to take this course, regardless of background. To get the most out of this course, we recommend that you:
> Completed an introductory course in machine learning (like 6.036 / 6.390). To learn this on your own, check out Andrew Ng’s ML course, fast.ai’s ML course, or Dive into Deep Learning.
Does anyone have a recommendation for one of these options over the others?
- nxpnsv 4y agoI enjoyed Ng's course a lot. Fast.ai is also good, a little more demanding imho. Didn't try the dive...
- eachro 4y agoDepends on your background and what you're most interested in learning. - if you want to really understand the mathematical foundations, you should take Andrew Ng's course (the full Stanford course not the Coursera one) - If you want a quick sampler of ML and to know the big ideas without investing too much time, take Andrew Ng's coursera course. I'm not sure what his deeplearning.ai course is like but it seems like that one is in python. - if you want to learn by getting your hands dirty, either fast.ai's ML course or Dive into Deep Learning is good. Fast.ai's course will get you into the nitty gritty parts of training neural networks.