Probability Theory: Creating Bayesian Models

Bayesian models are the perfect tool for use-cases where there are multiple easily observable outcomes and hard-to-diagnose underlying causes using a combination of graph theory and Bayesian statistics. Use this course to learn more bout stating and interpreting the Bayes theorem for conditional probabilities. Discover how to use Python to create a Bayesian network and calculate several complex conditional probabilities using a Bayesian machine learning model. Youll also examine and use naive Bayes models which are a category of Bayesian models that assume that the explanatory variables are all independent of each other. Once you have completed this course you will be able to identify use cases for Bayesian models and construct and effectively employ such models.