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
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discover the key concepts covered in this coursedefine and understand the Bayes theoremenumerate the architecture of Bayesian networksuse the chain rule with Bayesian networkscreate probability tables for a Bayesian networkexplore the probability tables of nodes in a Bayesian networkquery Bayesian networks to measure probabilities
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define a Bayesian model in Pythonpredict values with Bayesian modelsexplore probabilities associated with a Bayesian modelcreate naive Bayes models in Pythonpredict values with naive Bayes modelssummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 32sUP NEXT
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2.Bayes Theorem10m 33s
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3.Bayesian Networks13m 17s
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4.Using the Chain Rule with Bayesian Networks9m 15s
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5.Creating a Bayesian Network Model11m 33s
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6.Associating Probabilities with Bayesian Networks10m 3s
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7.Computing Probabilities from Bayesian Networks6m 34s
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8.Creating Bayesian Machine Learning Models8m 12s
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9.Predicting Values Using a Bayesian Model9m 47s
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10.Interpreting Probabilities Generated by Bayesian Models5m 15s
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11.Understanding and Creating Naive Bayes Models7m 20s
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12.Testing Naive Bayes Machine Learning Models8m 43s
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13.Course Summary1m 58s
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