Join Keith McCormick for an in-depth discussion in this video Bayesian networks, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Okay, time to talk about Bayesian networks.…Once again, we're encountering a technique…that's quite different from the others.…Of course, you're going to encounter the term…Bayesian or Bayes' throughout statistics.…It really is a whole branch of statistics.…We're gonna focus on Bayesian networks,…but Bayes' theorem is really just about a way…of combining or updating probabilities…as you get more information.…
It's really about conditional probability.…If you know more than one thing about someone,…then your overall assessment changes.…We're gonna discuss three different ways…of addressing interaction, so that's gonna prove…to be an important theme of our discussion…of Bayesian networks.…Bayesian networks will be one of the three ways.…Naive Bayes, an algorithm that you may,…or approach, that you may have heard of…and Bayesian networks are not exactly the same.…
We're gonna make brief mention of Naive Bayes…and talk more about Bayesian networks.…One of the things that truly unique about Bayesian networks…
Note: These tutorials are focused on the theory and practical application of binary classification algorithms. No software is required to follow along with the course.
- Why do you need classification?
- Statistical algorithms versus machine learning algorithms
- Combining models using ensembles
- Classification modeling challenges
Skill Level Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
1. The Big Picture: Defining Your Classification Strategy
2. How Do I Choose a "Winner"?
3. Algorithms on Parade
4. Common Modeling Challenges
Next steps3m 17s
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