However, their Deep Learning course was nowhere near as helpful. The instructor dove into complex topics and skimmed them lightly without offering much context. The exercises required me to do a lot of research outside the course to complete them. I essentially had to teach myself the topic. If the course had cost money I would've wanted a refund.
Yes. Python has the best collection of sufficiently user-friendly and fast modules for machine learning. Other languages tend to have fast, friendly, or many modules, but not all three. I suppose R is somewhat competitive on those aspects, but R isn't a great general-purpose language.
Python is primarily used because the machine learning libraries within it are very mature and play nicely with each other.
It is easy to get started in Python (and most of its libraries) by downloading the freely-available Anaconda distribution. This usually "just works", cross-platform. The language itself is extremely straightforward to pick up.
Within the Python ecosystem there are many mature libraries. In particular NumPy was written for carrying out vectorised computation. This enabled more libraries, such as pandas (for dataframe manipulation), SciPy (for general scientific computation) and scikit-learn (for ML) to be developed. Each of these libraries also possess clean and consistent APIs for carrying out their specialty tasks.
Thus it becomes straightforward in Python to import data from many sources, "wrangle" it into the correct format (even with real-world, messy data), put it into an ML data pipeline and then visualise it easily (via Matplotlib or Seaborn). In addition there is Jupyter for straightforward "notebook" style research.
Finally, Theano and TensorFlow are two great deep learning libraries. There are a few hiccups on installation sometimes, but for the most part they "just work".
There are still some "missing pieces" however. The statsmodels library does a good job of time series analysis, but it doesn't yet compete fully with R in this respect.
Julia is also likely to make serious inroads into Python's usage in the near future. I'm excited about where the project is heading.
I am hoping that udacity is able to keep this sort of pricing. I am also hoping that the udacity doesn't get subsidized through student loans like ITT was. I think it skews things so student and education company goals are no longer aligned. They have an interest in churning out as many students as possible with little quality control then.
By making the student cough up a good but not insurmountable chunk of cash upfront udacity has to ultimately answer to the student market.