Enroll Course: https://www.coursera.org/learn/introduction-to-pymc3
If you’re looking to deepen your understanding of probabilistic programming and Bayesian inference, the Coursera course “Introduction to PyMC3 for Bayesian Modeling and Inference” is an outstanding resource. This comprehensive course is designed for learners who want to leverage Python and Jupyter notebooks to build scalable Bayesian models.
The course is structured into four detailed modules, starting with an introduction to the PyMC3 framework, where students learn the syntax and visualization tools like ArViz. As the course progresses, learners explore regression and classification problems, including handling outliers and creating hierarchical models, culminating in a real-world case study. The final module focuses on metrics for assessing model quality, debugging techniques, and applying these skills to model COVID-19 case dynamics using a SIR model.
What sets this course apart is its practical approach, with hands-on examples and downloadable notebooks, making complex concepts accessible and applicable. Whether you’re a data scientist, statistician, or researcher, this course provides valuable skills in Bayesian modeling that can be directly applied to various fields.
I highly recommend this course for anyone interested in probabilistic programming, especially those looking to incorporate Bayesian methods into their data analysis toolkit. It’s a well-structured, engaging, and practical course that bridges the gap between theory and real-world application.
Enroll Course: https://www.coursera.org/learn/introduction-to-pymc3