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.
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).
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.
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?
To learn a solid theoretical foundation: Caltech's "Learning from data" [1]. It is one of those rare courses where the professor is so good that he manages to make tricky concepts seem almost trivial (like Paar's "Understanding cryptography").
Look elsewhere if all you want is to learn tools and start practical projects ASAP without really understanding what you are doing. Tools come and go and will serve you for a while, concepts are timeless and will serve you for life. You need both, of course, but I wouldn't skip the theory, specially when such an amazing course is on tap.
Don't worry about the math, one of the great things about this course is that the professor is a magician when it comes to explaining and defanging scary looking math, you'll be fine. Having said that, it's mostly basic matrix and vector algebra, basic probability and basic calculus (all high school level).
I haven't taken any of these courses. There were these courses from Amazon https://aws.amazon.com/training/learning-paths/machine-learn... that were highly recommended. But it all comes down to what you really want. If you just want to get a bird's eye view of the ML algorithms buffet for you to implement one of their Black box models then they are a good place to start. But for anything deeper, I would go with Charlysl's recommendation.
The online Stanford courses--CS224, CS229, CS231--are an excellent introduction into modern AI. CS231 with Andrej Karpathy in particular was a game-changer for me. It has three very thorough and well-designed assignments that will have you implement many of the basic algorithms discussed in the course.
The pre-requisite linear algebra for these subjects can be learned through Gilbert Strang's MIT course. A basic grounding in statistics and probability theory, along with calculus will also help.
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).