+1 to Sutton & Barto. There is a second edition of their book available; see a link at https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html.
https://www.udacity.com/course/reinforcement-learning--ud600 is also good; it is not super-modern, but it is easily accessible, and it covers a large part of classical RL - this helps if you want to read recent RL papers because terminology and ideas become more familiar.
I'm not sure Andrey Karpaty's blog post (linked in the article) is a good intro to RL - it starts with Policy Gradient which is on a complex side of RL techniques spectrum. From other sources I've heard Policy Gradient is harder to get working on an arbitrary problem than e.g. Q-Learning, but don't quote me on that :)
If one doesn't want to dive deep in RL, and just want to start with recent methods then https://www.nervanasys.com/demystifying-deep-reinforcement-l... blog posts provide a very good starting point.
I'd recommend new Coursera Washington University ML course (https://class.coursera.org/machlearning-001/lecture) as a solid foundation. You'll learn key ML concepts (bias vs variance, bayes theory etc.) and the lector is simply the best I've seen.
Following that Ian Goodfellow book http://www.deeplearningbook.org/ is very good.
If you want to learn foundations of Reinforcement Learning I'd recommend Sutton & Barto https://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.h...