Lecture 1 | Machine Learning (Stanford)

What people said

11001 · 2014-01 · Strong recommendation

I'd suggest going back to the original youtube[1] and course materials[2]. Coursera version is nothing but a hand-wavy watered down "feel good" version of the original class. I also really like the Caltech's take "Learning from data"[3] [1] [VIDEO] [2] [LINK] [3] [LINK]

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joaogui1 · 2020-01 · Recommendation

Machine Learning: * [VIDEO] : Andrew Ng's machine Learning course, the recommended entry point by most people * [LINK] : More kaggle focused, but also more modern and has interesting projects Do both courses simultaneously, take good notes, write useful flashcards, and above all do all the exercises and projects Deep Learning * [LINK] - Very hands-on, begin with " Practical Deep Learning for Coders" and then "Advanced Deep Learning for coders" * [LINK] : More bottom-up approach, helps to understand the theory better Do those two courses in parallel (you can try 2 weeks of coursera followed by one of fastai in the beginning, and then just alternate between them), take notes, write good flashcards and above all do the exercises and projects. After that you will be done with the beginning, your next step will depend on what area interested you the most, and getting way too many resources right now can be extremely confusing, so I would recommend doing a follow-up post after you worked through the above resources. Also as non-ML stuff I recommend Scott Young's Ultralearning and Azeria's self improvement posts ( [LINK] )

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weishigoname · 2016-10 · Recommendation

there are some lectures about machine learning on youtube. I think they are good to watch: [LINK] and [VIDEO] and my favorite channels are Standford and MIT open courseware [LINK] and [LINK]

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