This course offers a comprehensive exploration of the Bayesian approach to statistics and machine learning, known for its logical, flexible, and intuitive nature. Throughout the course, you will gain expertise in engineering and analyzing a range of foundational Bayesian models that can be applied across various domains. These models encompass both basic one-parameter models and more advanced multivariate and generalized linear regression models. The increasing popularity of Bayesian models is closely tied to the growing availability of computing resources necessary for their implementation. In this course, you will leverage one such resource, the rjags package in R. By combining the capabilities of R with the JAGS (Just Another Gibbs Sampler) engine, rjags provides a robust framework for Bayesian modeling, inference, and prediction. By the end of this course, you will have acquired the skills and knowledge needed to effectively utilize Bayesian models in your statistical and machine learning endeavors.