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Comment by davmre | Hacker News Reader

Andrew Ng's Coursera ML course is supposed to be pretty accessible. I've also heard good things about Machine Learning for Hackers (http://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/d...).

Ultimately, ML is a mathematical discipline. You can ask for a gentle approach that gets you to the foot of the mountain, but "if you want to learn about nature, to appreciate nature, it is necessary to understand the language that she speaks in." If you want to be more than an amateur, there's not much substitute for getting comfortable with math at the level of, say, Kevin Murphy's book.

The good news is that the required math is fairly elementary - calculus, linear algebra, probability and statistics, all freshmen or maybe sophomore-level topics - so it shouldn't be beyond reach of a motivated developer able to set aside some time to learn. MOOCs and organizing study groups with friends/co-workers can help a lot here as well.

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I'm currently taking Andrew Ng's Coursera course and I'd agree it's quite accessible. In fact, if you have a solid understanding of calculus and linear algebra, you might find it a bit slow at times.

This was my problem; it was incredibly boring (at least for the first few classes) and the video-based nature of it meant that I had to skip around and try to pick up what was going on later, which seldom worked. I hear it's more self-paced now, so that might be a better option these days.

For people who are disappointed by the shallowness of it, I recommend supplementing it with the notes to his Stanford class: http://cs229.stanford.edu/materials.html. The combination worked well for me.

so it shouldn't be beyond reach of a motivated developer able to set aside some time to learn. MOOCs and organizing study groups with friends/co-workers can help a lot here as well.

I'm in the middle of that process right now. I only took Calc I in college and that was 20 years ago, so I have decided to work my way through a Calc sequence, Differential Equations, Linear Algebra, and Probability and Statistics through a combination of MOOCs, "X For Dummies" books, Youtube videos (hello, Gilbert Strang!), Schaum's Outlines books, Khan Academy, a mammoth stack of college maths texts that I've picked up at used book stores, and questions on stats.stackexchange.com, math.stackexchange.com, learnmath.reddit.com, etc.

I'm doing the Ohio State MOOC on Calc I now on Coursera, and accompanying that with the Gilbert Strang "Highlights of Calculus" video series[1]. So far so good. I definitely think this stuff is learnable if one is willing to put in the time and work, even without going back to taking "on campus" classes at a university.

[1]: https://www.youtube.com/playlist?list=PLFW_V3qDH5jRyfpD9uiq6...

I'm a high school Senior (17) and I'm currently taking it. It is ridiculously understandable and I often see myself yearning for more. But I've also had Calc 3 + Linear Algebra by now, so its understandable that not everyone would get it. The intuition is simple, however.

Yeah I'd like to second this. This course specifically is what opened the door for me.

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