I'm biased, because I took the course when it was called "ML Class" in 2011:
https://www.coursera.org/learn/machine-learning
Everything is done in Octave (ie - open-source matlab-like language); primitives are vectors and matrices - so you'll have to wrap your head around that.
But that course gave me the first explanation as to how neural networks actually worked that I could understand; I had been reading about neural networks for years from various sources - books, online, videos, etc - and nothing ever "clicked" for me (mainly around how backprop worked). For some reason, this did it for me.
Since then, I have taken other MOOCs centered around ML and Deep Learning, mainly with a focus on self-driving vehicles.
Oh - ML Class also led one individual to implement this during the course, as the ALVINN vehicle was mentioned in more than a few ways:
https://blog.davidsingleton.org/nnrccar/
While Singleton does mention its "vintage-ness", I still think it's a sound project for inspiration and learning how to apply a neural network to a simple self-driving vehicle system, not to mention the fact that it replicated a system from the 1980s using today's commodity hardware; I recall reading about ALVINN when I was a kid, with wonderment about how it "worked" - it was one of several 1980s projects in the space that got me hooked on wanting to learn how to make computers learn.