Learn the basics of Bayesian linear regression using the excellent PyMC Probabilistic Programming package. This focuses on model formulation in PyMC, interpretation, and how to make predictions on out-of-sample data.
Replicating the cat mood classifier, this time using Julia and Flux.jl.
Things we learn here include image data exploration, transfer learning, custom datasets, comparing ML models, saving/loading models and model data, conditional setup for different work environments.
In this project, election data is collected, explored and cleaned. Visualization functions are refactored to be used on a dashboard later on.
The cleaned data is loadsed and the plot generating functions are refactored here. Then a dashboard is created using dash.
This tutorial covers the fundamentals of Bayesian approaches to time series, model construction, and practical implementation, using real-world data for hands-on learning.