If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren't otherwise tractable with classical methods.

Bio:

Lara is a risk specialist with the Federal Reserve Bank of Chicago and occasional adjunct at the University of Chicago's Booth School of Business, teaching Python and R. Previously she's taught a data science bootcamp and built risk models for large financial institutions at McKinsey & Co. She enjoys coffee, yoga, and bikes.