Probability Theory: Creating Bayesian Models

Math    |    Expert
  • 13 videos | 1h 44m 1s
  • Includes Assessment
  • Earns a Badge
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

  • 1m 32s
  • 10m 33s
  • Locked
    3.  Bayesian Networks
    13m 17s
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    4.  Using the Chain Rule with Bayesian Networks
    9m 15s
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    5.  Creating a Bayesian Network Model
    11m 33s
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    6.  Associating Probabilities with Bayesian Networks
    10m 3s
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    7.  Computing Probabilities from Bayesian Networks
    6m 34s
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    8.  Creating Bayesian Machine Learning Models
    8m 12s
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    9.  Predicting Values Using a Bayesian Model
    9m 47s
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    10.  Interpreting Probabilities Generated by Bayesian Models
    5m 15s
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    11.  Understanding and Creating Naive Bayes Models
    7m 20s
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    12.  Testing Naive Bayes Machine Learning Models
    8m 43s
  • Locked
    13.  Course Summary
    1m 58s

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