How much time does the course use up? I expect everyone is different - so it's a personal question of your time. Are there homework assignments or exercises? How are the practical aspects handled for example with learning a programming language?
I took 2 classes, which is a huge time-sink (I can't even go through the textbooks). I think for a person with a full-time job, 1 course would be enough for a particular semester.
On top of this, writing the Octave code lets me see line by line what the algorithm is doing as well, and it's a quick way to gain insight into the ML approaches by seeing exactly what each intermediate step is doing.
Finally, even when it's a "translation" I find that it's not that trivial to do. Seeing a formula on a page and being able to write that as vectorized MATLAB code has also been an interesting challenge.
Although I've been struck by the fact that they expect most people to do loop iteration until forced into vectorized concepts - I've been forcing myself to do it all as linear algebra from the start, as that's what I see as the point of the class.
also, for some more fun you may want to check out Stanford's uldl page as well, which goes into unsupervised learning and deep-learning architectures.
But otherwise, it is really a great course - such an amount of practical information in such a short time.
Although it would be cool to organize a showcase of such projects spontaneously!
For those who can only afford to take one or two classes per semester, keep them coming!