Probabilistic Graphical Models (PGMs) In Python | Graphical Models Tutorial | Edureka

(** www.edureka.co/graphical-modelling-course **)
This Edureka "Graphical Models" video answers the question "Why do we need Probabilistic Graphical Models?" and how are they compare to Neural Networks. It takes you through the basics of PGMs and gives real-world examples of its applications.

[01:02] Why do you need PGMs?
[01:40] What is a PGM?
[09:59] Bayesian Networks
[21:22] Markov Random Fields
[23:43] Use Cases
[25:02] Bayesian Networks & Markov Random Fields
[28:21] PGMs & Neural Networks


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How it Works?
This is a 3 Week Instructor led Online Course, 15 hours of assignment and 10 hours of project work
We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!

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About the Course

Graphical Models Course is designed to teach Graphical Models, fundamentals of Graphical Models, Probabilistic Theories, Types of Graphical Models – Bayesian (Directed) and Markov’s (Undirected) Networks, Representation of Bayesian and Markov’s Networks, Concepts related to Bayesian and Markov’s Networks, Decision Making – theories and assumption, Inference and Learning in Graphical Models.

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Who should go for this course?

People who are interested/working in the Data Science field and have a basic idea of Machine Learning or Graphical Modelling, Researchers, Machine Learning and Artificial Intelligence enthusiasts.


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Why learn Graphical Model?

Machine Learning is a Probabilistic Perspective. You'll see more often than not, that many machine learning models are defined with graphical models. That make it an essential aspect in your learning path towards Data Science.

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