#299 overall
Practical Machine Learning
fast.ai
2 recommenders · 2 threads · recommended in 2 years, 2018–2023 · score 1.5
A free, practical introduction to machine learning from fast.ai, taught by Jeremy Howard, focused on hands-on how-to with models and code rather than theory. Videos can be followed at your own pace.
Commenters recommend it for learning the how-to of ML and getting into the nitty gritty of training neural networks, with little time spent on theory.
Machine Learning & AIbeginnerfreevideo lectures#practical ml#hands-on#neural networks#howard#fast.ai
Recommendations by year
20062026
Depends 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.
If you have at least some coding experience and you are interested in the practical aspects of ML/DL (i.e., you want to learn the how-to, not the why or the whence), my recommendation is to start with the fast.ai courses by Jeremy Howard (co-author of this "Matrix Calculus" cheat sheet) and Rachel Thomas[a]:
* fast.ai ML course: http://forums.fast.ai/t/another-treat-early-access-to-intro-...
* fast.ai DL course: part 1: http://course.fast.ai/ part 2: http://course.fast.ai/part2.html
The fast.ai courses spend very little time on theory, and you can follow the videos at your own pace.
Books:
* The best books on ML (excluding DL), in my view, are "An Introduction to Statistical Learning" by James, Witten, Hastie and Tibshirani, and "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. The Elements arguably belongs on every ML practitioner's bookshelf -- it's a fantastic reference manual.[b]
* The only book on DL that I'm aware of is "Deep Learning," by Goodfellow, Bengio and Courville. It's a good book, but I suggest holding off on reading it until you've had a chance to experiment with a range of deep learning models. Otherwise, you will get very little useful out of it.[c]
Good luck!
[a] Scroll down on this page for their bios: http://course.fast.ai/about.html
[b] Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ The Elements of Statistical Learning: https://web.stanford.edu/~hastie/ElemStatLearn/
[c] http://www.deeplearningbook.org/