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I'm curious -- does one need to consult these papers when using algorithms from libraries such as SciKit (Python)? I ask because the field seems so broad, and

• agibsonccc 12y ago
  Hi Machine learning instructor from Zipfian Academy here. We teach scikit learn at our bootcamp and people who have some sort of scientific background, have completed Ng's course, or are software engineers typically apply to our program. What we have found in the class room is that if you understand the fundamentals of machine learning algorithms, you don't have to understand all the minutiae of an algorithm as a user. The hardest problem to solve is figuring out how to encode data such that the algorithm learns properly as well as all the pre processing steps that typically go in to machine learning. The only thing that would be worth understanding for an algorithm are the hyperparameters associated with the respective models. An example of this would be learning rate or regularization coefficients. If you understand what those are useful for, you should be fine.
• nandemo 12y ago
  FWIW, I'm studying ML with Introduction to Statistical Learning, which was recommended to me by several people. Personally I didn't enjoy Coursera's ML course (though I believe Ng is a great teacher) and dropped out, but I'm enjoying this book. It's available as PDF for free: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/