[sidkhanooja]

Contrary to popular opinion, I find that Andrew Ng's Intro to ML (on Coursera/Stanford) focuses too much on basic math and theory - which doesn't detract from the course's quality, but makes the course a drudgery to go through.

Programming exercises involve a single line or two, and that too in Octave - which was all the rage back when the course was launched, but it's not so useful now.

Instead, start with this - https://www.fast.ai

It emphasizes practicality to the extreme - you are only taught theory/domain knowledge when needed. The instructor's amazing, the massive scale of knowledge imparted boggles the mind, and you feel like you've accomplished something when you're finished with it.

Best of all, it's free. And you can start Deep Learning there too when you're done with ML, if you feel the need (or interest).

[k4ch0w]

I'd second this class. I have done both and if you're brand new to machine learning I'd do fast.ai first. You gain a greater understanding from Andrew's course, but if you're not going to become a practitioner fast.ai is better.

[ihvck]

I really love the code first approach of Fast.ai, I'm impressed by teaching style of Jeremy its clear, succinct, and to the point.

[philonoist]

There should also be some sort of polls for these sort of questions or at least for mapping goals. I past polls if any in ...yc.com/news feed. How do I go about it?

[jackallis]

i have to agree with you here. It's good class if you want to focus on theory and veer towards academic part of ML.