There has been uprising of probabilistic programming and Bayesian statistics. These techniques are tremendously useful, because they help us to understand, to explain, and to predict data through building a model that accounts for the data and is capable of synthesizing it. This is called the generative approach to statistical pattern recognition. Estimating the parameters of Bayesian models has always been hard, impossibly hard actually in many cases for anyone but experts. However, recent advances in probabilistic programming have endowed us with tools to estimate models with a lot of parameters and for a lot of data. In this tutorial, we will discuss two of these tools, PyMC3 and Edward. These are black box tools, swiss army knifes for Bayesian modeling that do not require knowledge in calculus or numerical integration. This puts the power of Bayesian statistics into the hands of everyone, not only experts of the field. And, it's great that these are implemented in Python with its rich, beginner-friendly ecosystem. It means we can immediately start playing with it. We have planned three awesome parts, spread over three awesome hours: First hour: Introduction to Bayesian machine learning. Second hour: Baby steps in PyMC3 and Edward. Third hour: Solve a real-world problem with PyMC3 or Edward (model, fit, criticize).

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