| I'll tell you how I started my journey: I took the Stanford ML Class in 2011 taught by Andrew Ng; ultimately, Coursera was born from it, and you can still find that class in their offerings: https://www.coursera.org/learn/machine-learning On a similar note, Udacity sprung up from the AI Class that ran at the same time (taught by Peter Norvig and Sebastian Thrun); Udacity has since added the class to their lineup (though at the time, they had trouble doing this - and so spawned the CS373 course): https://www.udacity.com/course/intro-to-artificial-intellige... https://www.udacity.com/course/artificial-intelligence-for-r... I took the CS373 course later in 2012 (I had started the AI Class, but had to drop out due to personal issues at the time). Today I am currently taking Udacity's "Self-Driving Car Engineer" nanodegree program. But it all started with the ML Class. Prior to that, I had played around with things on my own, but nothing really made a whole lot of sense for me, because I lacked some of the basic insights, which the ML Class course gave to me. Primarily - and these are key (and if you don't have an idea about them, then you should study them first): 1. Machine learning uses a lot of tools based on and around probabilities and statistics. 2. Machine learning uses a good amount of linear algebra 3. Neural networks use a lot of matrix math (which is why they can be fast and scale - especially with GPUs and other multi-core systems) 4. If you want to go beyond the "black box" aspect of machine learning - brush up on your calculus (mainly derivatives). That last one is what I am currently struggling with and working through; while the course I am taking currently isn't stressing this part, I want to know more about what is going on "under the hood" so to speak. Right now, we are neck deep into learning TensorFlow (with Python); TensorFlow actually makes things pretty simple to create neural networks, but having the understanding of how forward and back-prop works (because in the ML Class we had to implement this using Octave - we didn't use a library) has been extremely helpful. Did I find the ML Class difficult? Yeah - I did. I hadn't touched linear algebra in 20+ years when I took the course, and I certainly hadn't any skills in probabilities (so, Kahn Academy and the like to the rescue). Even now, while things are a bit easier, I am still finding certain tasks and such challenging in this nanodegree course. But then, if you aren't challenged, you aren't learning. |