Here is what I did. I don't know how good your programming skill is so I am going to start from the very beginning.

1. Take the intro python course first from MIT's edx.6.001x. Don't be alarmed that it's a course from a big name school. It will be hard but not impossible to finish. Some concepts will be foreign or straight up weird. Take it again if you have to, I am a slow learner I think I took the course 3 times before actually completely finishing it which is on the slow side.

2. During this time brush up your math skills: Linear algebra for matrix multiplications, dot product and you should also learn how to read Greek letter formulas. This will help you build future intuitions on why our trusty for loops are no longer useful. Learn basic statistics concepts like mean square error, std, variance, distributions etc. You can also go into Calculus to learn the chain rule and how to derive gradients. TBH I didn't do this step very well, I did enough to help me understand the concept of backpropagation and gradient descent but if you ask me to manually derive everything I will have a hard time doing so.

3. Once you are able to understand other people's code in python I recommend taking the Udacity ML course instead. Normally every one starts with the Coursera course but they use octave instead of python. I was constantly frustrated from debugging my octave code while trying to learn these concepts I didn't really enjoy the course. You can always watch the videos but please save your time and skip octave if you already know python.

4. Now you should know enough to start a Personal Project, this part is where you actually start to learn what you are doing. It sounds silly I know, you can watch 10 thousand hours of video, copy other's code and it will not beat the experience gained from just 1 hour of hands-on practice. You should find some code that interests you on Github, type everything out while taking notes, then recall the information back on why the person did that particular thing in his code. All the previous lectures and videos are just to get you started to understand the reasons behind someone else's workflow. Other people's work will also help you build intuition and reference points. The real learning starts when you begin to absorb someone else's concept then turn it into your own so you can work on your own projects.

4.5) [Optional] There are tons of specialized fields in ML, you should have enough foundations and intuitions to go in more specialized fields. eg computer vision, robotics etc.

5) Read Papers to keep yourself updated on the latest discoveries.

Finally, repeat step 4 through 5 for profit.

Edited: Once you understand the fundamentals you should start to learn different frameworks to increase your productivity. Sklearn is very popular for basic ML, for computer vision there are tensorflow, theanos, keras, pytorch etc.

Edited: grammar

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can't you apply supervised learning to a situation with delayed rewards and still get the same or similar results? You can apply semi/self-supervised learning to predict future rewards. You cannot apply supervised learning to maximize rewards. A pure observer is useless. If you want a job done, you need an agent. And you can neither hardcode nor supervised train an agent because you don't know the details of the environment. All you can do is hardcode the goal into a reward function and use reinforcement learning to reach it. Of course, you could solve OpenAI Gym toy environments with supervised learning or even hardcoding, because you know these toy environments and they will not change. More on reddit.com

That's great! Not to get your hopes down, but unfortunately that course is a little too simple. If you want to get to the nitty-gritty of ML, I suggest checking out Standford CS229 on YouTube, also by Andrew NG. It's a lot more rigorous and comprehensive More on reddit.com

Supervised learning is learning from labeled data. Unsupervised learning is learning from unlabeled data. Self-supervised learning is learning from unlabeled data with learned labels. Bonus: Weakly supervised learning is learning from poorly labeled data or vaguely labeled data. Semi-supervised learning is learning from some labeled data and some unlabeled data. More on reddit.com