Here is a course on Probabilistic Programming with PyMC3. 

We've included plenty of examples. You might want to have a look.

Real world case studies in this course include
  • Sports Analytics
  • Policy decision analysis
  • AB Testing
Key learning points will be (in no particular order)
  • What is PyMC3 and understanding how variables are stored and how to build models
  • How to debug and evaluate models
  • What is MCMC and how to choose a sampler and evaluate the performance of respective samplers. 
  • What is Theano, how to debug Theano. 
  • How to write specialised functions in Theano and integrate these with PyMC3
  • Case studies from a range of areas, how to apply these and including some ideas for your day job.
  • The PyMC3 modelling process - unseen material.

Do I need to set up my environment?
  • I've taken care of all the environment stuff at the github repo here - just launch the binder link.
  • We have some videos on installing PyMC3 if you want to do that but it's optional.

PPP | Probabilistic Programming Primer | Bayesian Stats | PyMC3

A course by Peadar Coyle, a core PyMC3 contributor and founder of Aflorithmic Labs

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