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Comment by kyro | Hacker News Reader

Is this worth going through over picking up a textbook or two? I've found that Coursera courses are actually quite bloated. Lots and lots of empty talking, and very little substance.

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I've taken the Coursera ML class. It's very easy if you have the adequate math background. And it's not very comprehensive, there are lots of machine learning methods that are not covered. So it's more like an introductory course to machine learning.

But it's absolutely commendable how Andrew Ng takes the topic to such an understandable level that a clever high schooler who knows a little about programming could take the course. There's even extra videos serving as a crash-course to linear algebra and octave programming in the first week.

So he really manages to make the topic accessible to a very large audience.

>adequate math background

Do you know what kind of math is needed other than linear algebra.

Haven't done the course, but from the preview of the videos, they cover the math you need and it's just basic, high-school level knowledge of linear algebra.

Basic vector and matrix operations. The first half of a typical freshman linear algebra course is more than enough. But like I said, there is a matrix review in the beginning, so if you're willing to study those extra lectures, then almost no prior knowledge is needed.

Also being able to take derivatives helps in a couple of places, but is not necessary.

Thats linear algebra.

Some calculus and linear algebra. The majority is linear algebra.

For a beginner to machine learning I'd recommend Andrew Ng's course notes and lectures over any textbook I've seen. But I prefer his Stanford CS 229 notes to Coursera for exactly the reasons you state: they are watered down. After you really can understand Andrew Ng's course notes I'd recommend a textbook because they go in more detail and cover more topics. My two favorites for general statistical machine learning are:

* Pattern Recognition and Machine Learning by Christopher M. Bishop

* The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman

Both are very intensive, perhaps to a fault. But they are good references and are good to at least skim through after you have baseline machine learning knowledge. At this stage you should be able to read almost any machine learning paper and actually understand it.

Isn't Murphy's book more up to date and comprehensive as a reference?

Edit: Andrew Ng's Coursera course is CS229A (http://cs229a.stanford.edu/), not really watered down.

I'm a big fan of Murphy but its comprehensiveness means you lose some detailed explanations. Bishop really gets at those details (so does EoSL).

Depends on the course, but other a than a select few, I tend to agree. Not the case with edX courses, though, I think they are up to a much higher standard. (At least the programming/math ones).

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