Graphical Models Certification Training

Simplify machine learning with graphical modelling

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Key Highlights

  • 15 Hours of Live Instructor-led Classes
  • 5 sessions of 3 hours each on Weekends
  • Live Project on real-life Case Studies
  • Practical Assignments
  • Lifetime Access to Learning Management System
  • 24x7 Expert Support
  • Course Completion Certificate
  • Online Forum for Discussions
  • Cloud lab for hands-on experience

Course Price Range

$289.00 - $299.00 $600.00 - $600.00

Contact Us

1866-216-7898

(Toll Free)

Available Courses Delivery

This course is available in the following formats:

Virtual Live

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Upcoming Batches


Nov 22nd
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Delivery: Online
Access: Lifetime
Fri - Sat (4 Weeks)
Timings - 09:30 PM to 12:30 AM (EST)

Weekend Batch (Evening)
$600  $299
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Nov 30th
Delivery: Online
Access: Lifetime
Sat - Sun (3 Weeks)
Timings - 10:00 AM to 01:00 PM (EST)

Weekend Batch (Morning)
$600  $299
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filling status

Course Overview

Graphical Models Certification Training teaches the basics of graphical models, their types, representations, and related concepts. Candidates will primarily learn about Bayesian Networks (directed graphs) and Markov's Networks (undirected graphs). These are used for machine learning in intelligent systems, speech recognition, diagnostics, and more.

Course Objectives

  • Provide a brief introduction to graphical models, probability, and graph theory along with where they can be applied
  • Teach how to build a directed Bayesian Network on Python and discuss its advantages
  • Acquaint learners with undirected graphs (Markov Network) in machine learning
  • Give a thorough understanding of graphical model structures and its parameters

Career Benefits

  • Better skills in machine learning
  • Bigger opportunities in the Artificial Intelligence industry
  • Work on advanced and promising AI projects that use machine learning
  • Better remuneration with newly acquired AI skills
  • Improve business processes using created/enhanced intelligent system

Prerequisites

  • None, but skills in Python programming and knowledge in statistics will make the course easier.

Who should take up?

  • Data science enthusiasts
  • Developers interested in machine learning
  • Python programmers interested in AI
  • Inexperienced machine learning engineers

Course Content

  • 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
  • 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
  • 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
  • Inference
  • Complexity in Inference
  • Exact Inference
  • Approximate Inference
  • Monte Carlo Algorithm
  • Gibb?s Sampling
  • Inference in Bayesian Networks
  • 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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