CS229A is Stanford's applied machine learning course taught by Andrew Ng, focusing on practical ML methods and how to apply them to real problems rather than on the deeper theory covered in CS229.

Commenters praise Andrew Ng's teaching as making machine learning engaging and practical, and one contrasts it favorably with Geoffrey Hinton's neural network course.

What commenters said (1)

1. Recommendsdenzil_correa2017in “Jennifer Widom Named Dean of Stanford School of Engineering”

   While I do understand that it may have felt a bit theoretical and heavy, I also think that this theory is more important to understand in neural networks than in other areas of ML e.g. for debugging issues.

   This is the same argument people gave for Machine Learning but Andrew Ng showed us otherwise.

   I felt that Andrew Ng left out a lot of the details that I was curious about.

   This is exactly the beauty of Ng's ML course. The fact that Andrew Ng made you curious makes CS229A (Applications of Machine Learning) a very successful course. Now, if you want to satiate that curiosity I will recommend you CS229 [0]. It contains all the theory and math and YET that course is as beautiful as CS229A. This further proves the point that you do not need theory to be boring to achieve your objectives. If you want to teach something, you will find a way. Au contraire, Jeff Hinton's course makes me not want to even touch or understand Neural Networks again. FYI, I have a Ph.D in Computer Science and I work as a Data Scientist. If this is my view, imagine what it would be like beyond the us elites?

   [0] https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

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