[Jabbermonkey]

If you're genuinely eager to learn probabilistic programming then by far the best resource I've found is a book called Statistical Rethinking by Richard McElreath. His (almost finished) draft for the second edition is up here: http://xcelab.net/rmpubs/sr2/statisticalrethinking2_08dec19....

Dr. McElreath also posts his lectures on youtube. The R code in the book and his lectures use a library/package he wrote which provides a wrapper to simplify building Stan models. The code has also been translated to PyMC3 on Python.

Code, slides, lecture videos are all referenced here: https://github.com/rmcelreath/statrethinking_winter2019

It might look like a huge amount of content but this course leads you very gently through key concepts, keeping the mathematics to a minimum. Don't be put off if you don't know the R language. The concepts are more important than the programming language and the code examples are kept simple.

If you make it through Statistical Rethinking then you might consider picking up Doing Bayesian Data Analysis by John Kruschke (a.k.a. the puppies book). I've found DBDA to be heavier going than SR but Kruschke takes a different approach to McElreath which can be useful if you get stuck on a concept, need more detail or just want a different angle on the subject.

[p1esk]

But you haven't answered OP's question. Why would one want to invest several months of learning PP without seeing any clear examples of how it could be useful?