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Graphical Models Certification Training

985+ Learners

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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Machine Learning Graphical Models

Jul 18 Sat,Sun (4.5 Weeks) Weekend Batch Filling Fast 03:00 PM  05:00 PM
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Course Price at

$ 399.00

About 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.

Who should go for this training?

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.

Pre-requisites

Knowledge on Probability theories, statistics, Python, and Fundamentals of AI and ML 


Key Skills

Python, Artificial Intelligence, Graphical Model, Decision theory, Bayesian Network, Markov’s Networks, Factor Graph, Uncertainty, Hidden Markov Models, Inference, Monte Carlo Algorithm, Gibb’s Sampling, Model Learning

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Course Contents

Download Syllabus

Graphical Models Certification Training Content

Goal: To give a brief idea about Graphical models, graph theory, probability theory, components of graphical models, types of graphical models, representation of graphical models, Introduction to inference, learning and decision making in Graphical Models. 

Topics: 

  • Why do we need Graphical Models?
  • Introduction to Graphical Model
  • How does Graphical Model help you deal with uncertainty and complexity?
  • Types of Graphical Models
  • Graphical Modes
  • Components of Graphical Model
  • Representation of Graphical Models
  • Inference in Graphical Models
  • Learning Graphical Models
  • Decision theory
  • Applications


Goal: To give a brief idea of Bayesian networks, independencies in Bayesian Networks and building a Bayesian networks.
Topics:
  • What is Bayesian Network?
  • Advantages of Bayesian Network for data analysis
  • Bayesian Network in Python Examples
  • Independencies in Bayesian Networks
  • Criteria for Model Selection
  • Building a Bayesian Network

Goal: To give a brief understanding of Markov’s networks, independencies in Markov’s networks, Factor graph and Markov’s decision process.
Topics:
  • Example of a Markov Network or Undirected Graphical Model
  • Markov Model
  • Markov Property
  • Markov and Hidden Markov Models
  • The Factor Graph
  • Markov Decision Process
  • Decision Making under Uncertainty
  • Decision Making Scenarios

Goal: To understand the need for inference and interpret inference in Bayesian and Markov’s Networks.
Topics:
  • Inference
  • Complexity in Inference
  • Exact Inference
  • Approximate Inference
  • Monte Carlo Algorithm
  • Gibb’s Sampling
  • Inference in Bayesian Networks

Goal: To understand the Structures and Parametrization in graphical Models.
Topics:
  • General Ideas in Learning
  • Parameter Learning
  • Learning with Approximate Inference
  • Structure Learning
  • Model Learning: Parameter Estimation in Bayesian Networks
  • Model Learning: Parameter Estimation in Markov Networks

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What are the system requirements for this Graphical Models Certification Training?

The system requirement is a system with an Intel i3 processor or above, minimum 3GB RAM (4GB recommended) and an operating system either of 32bit or 64bit.

How will I execute practicals in this Graphical Models Certification Training?

Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment.

Certification

StepLeaf’s Graphical Models Professional Certificate Holders work at 1000s of MNC Companies All Over the World

FAQ

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

You will never miss a lecture at StepLeaf! You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
All the instructors at StepLeaf are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by StepLeaf for providing an awesome learning experience to the participants.
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