Enroll Course: https://www.coursera.org/learn/introduction-to-pymc3
Are you looking to dive into the world of Bayesian modeling and inference? If so, the “Introduction to PyMC3 for Bayesian Modeling and Inference” course on Coursera is an excellent choice, especially if you’re looking to complete a three-part specialization. This course serves as a capstone, bringing together the foundational knowledge you’ve likely gained in previous modules.
Throughout this course, Python and Jupyter notebooks are your trusty companions. You’ll start by getting acquainted with the PyMC3 framework, understanding its core concepts and syntax. The integration with the visualization library ArViz is a significant plus, offering intuitive ways to explore your models and their results. The course website, accessible at https://sjster.github.io/introduction_to_computation, provides further details and resources.
The syllabus is thoughtfully structured. The first part dives deep into PyMC3, covering essential modeling techniques and introducing ArViz for visualization. You’ll also find instructions for setting up your environment and running the notebooks, which is crucial for hands-on learning.
The second part tackles practical applications, demonstrating how to use PyMC3 for regression and classification problems. It doesn’t shy away from common challenges like handling outliers and building hierarchical models, culminating in a comprehensive case study.
Module three focuses on the critical aspect of evaluating your models. You’ll learn about various metrics and measures to assess the quality of your inferred solutions, with practical examples and visualizations to guide you. Debugging PyMC3 algorithms is also touched upon, a valuable skill for any practitioner.
Finally, the course culminates in an ungraded but highly rewarding project: modeling COVID-19 cases using a SIR model. This real-world application allows you to apply everything you’ve learned, inferring parameters from actual data. It’s a fantastic way to solidify your understanding and build a practical portfolio piece.
Overall, this course is a robust introduction to PyMC3. It balances theoretical understanding with practical implementation, making Bayesian modeling accessible and actionable. If you’re ready to leverage the power of probabilistic programming, this course comes highly recommended.
Enroll Course: https://www.coursera.org/learn/introduction-to-pymc3