Graphical Models Certification Training

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Ace Graphical Models with EDTIA'S Graphical Models Certification Training upskill your knowledge and technological skill. Rank at the top in machine learning and your career path.

Course Description

Graphical Models Course is prepared to guide candidates with 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 of Bayesian and Markov's Networks, Decision Making – theories and assumption, Inference and Learning in Graphical Models.

A graphical model is a probabilistic measure for which a graph represents the dependence structure between random variables. They are commonly used in probability theory, particularly Bayesian statistics and machine learning.

This certification course is for: Candidates working in the Data Science basic of Machine Learning Researchers Machine Learning Artificial Intelligence enthusiasts

Graphical models strive to explain concisely the possibly complex interrelationships between a set of variables. Moreover, through the description key, properties can be read instantly. The major concept is that a node in a graph represents each variable.

Knowledge of Probability theories Knowledge of statistics Knowledge of Python Fundamentals of AI and ML Statistics and Machine learning algorithms Python Essentials

Graphical models permit us to efficiently determine general message-passing algorithms that implement probabilistic inference. Hence we can answer queries without recording all settings of every variable in the model.

Graphical models are often used to model multivariate data; they allow us to represent high-dimensional distributions compactly; they do so by exploiting the interdependencies that typically exist in such data.

Graphical models are still utilized and will continue to be used in the future. They work well and are probably the best tool we have to work on unsupervised learning.

Graphical models have evolved into an incredibly widespread tool for modeling uncertainty. It provides a moral method to deal with delay through probability theory and an effective way to cope with intricacy through graph theory.

What you'll learn

  • In this course, you will learn: Basics of Graphical Model Bayesian Network Inference and more


  • Knowledge of Probability theories, Basics statistics, Python Fundamentals of AI and ML


Learn about Graphical models, graph theory, probability theory, components of graphical models, types of graphical models, representation of graphical models, inference, learning, and decision making in Graphical Models.

Why do we need Graphical Models?
Introduction to Graphical Model
Graphical Models for uncertainty and complexity?
Types of Graphical Models
Graphical Modes
Components of Graphical Model
Representation of Graphical Models
Inference in Graphical Models
Inference in Graphical Models
Decision theory

Understand Bayesian networks, independencies in Bayesian Networks, and build a Bayesian network.

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

Discover Markov's networks, independencies in Markov's networks, Factor graph, and Markov's decision process.

Illustration 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

Learn about the need for inference and interpret inference in Bayesian and Markov's Networks.

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

comprehend the Structures and Parametrization in graphical Models.

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


Edtia Support Unit is available 24/7 to help with your queries during and after Graphical Models Certification Training.

Graphical modeling languages use a diagram technique with detailed symbols conveying concepts and lines that link the symbols and define relationships and various other graphical notations to represent constraints.

To better understand Graphical Models, one must learn as per the curriculum.

The two most common forms of graphical models are directed and undirected graphical models, based on directed acyclic and undirected graphs, respectively.

The Graphical model is a subdivision of Machine Learning. A graph states the dependent need configuration between random variables.

Influence diagrams are generalizations of Bayesian networks that can represent and solve decision problems under uncertainty.

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

A graph permits us to outline the dependent relationships between the variables from the components of their parametric forms.

$141 $149
$8 Off

Training Course Features


Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.


For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.


Successfully complete your final course project and Edtia will provide you with a completion certification.

Graphical Models Certification Training

You will receive Edtia graphical model Training certification on completing live online instructor-led classes. After completing the course module, you will receive the certificate.

A graphical model Training certificate is a certification that verifies that the holder has the knowledge and skills required to work with graphical model technology.

By enrolling in the graphical model Training Certification course and completing the module, you can get Edtia Deep Learning with graphical model Certification.

Yes, Access to the course material will be available for a lifetime once you have enrolled in Edita graphical model Training Certification Course.

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