I'll throw my experience onto the pile of anecdotes in support of Ng's opinion. I've done both his Coursera class and another ML class that used Python. I star

I'll throw my experience onto the pile of anecdotes in support of Ng's opinion. I've done both his Coursera class and another ML class that used Python. I started the one class with zero Matlab/Octave experience, and the other with some Python but no real Numpy experience. Getting comfortable with Octave for doing basic vector/matrix operations took very little time, and within an hour I was able to just focus on learning the math. By contrast, I've been using Numpy on-and-off for a year now, and I still feel wedded to the reference manual. Keeping track of all the @$@#% types is definitely a chore. There may be technical advantages to doing things that way, but that doesn't make it any more learner-friendly. Nor does the Python community's habit of not bothering to specify types in the documentation help things in that department.