# Probability Theory: Creating Bayesian Models

Math    |    Expert
• 13 Videos | 1h 49m 31s
• Includes Assessment
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. You'll 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.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course define and understand the Bayes theorem enumerate the architecture of Bayesian networks use the chain rule with Bayesian networks create probability tables for a Bayesian network explore the probability tables of nodes in a Bayesian network query Bayesian networks to measure probabilities
• define a Bayesian model in Python predict values with Bayesian models explore probabilities associated with a Bayesian model create naive Bayes models in Python predict values with naive Bayes models summarize the key concepts covered in this course

## IN THIS COURSE

• 1.
Course Overview
• 2.
Bayes Theorem
• 3.
Bayesian Networks
• 4.
Using the Chain Rule with Bayesian Networks
• 5.
Creating a Bayesian Network Model
• 6.
Associating Probabilities with Bayesian Networks
• 7.
Computing Probabilities from Bayesian Networks
• 8.
Creating Bayesian Machine Learning Models
• 9.
Predicting Values Using a Bayesian Model
• 10.
Interpreting Probabilities Generated by Bayesian Models
• 11.
Understanding and Creating Naive Bayes Models
• 12.
Testing Naive Bayes Machine Learning Models
• 13.
Course Summary

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