Graphical Models Certification Training

0 (0 Ratings)

Graphical Models Certification Training

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 Assumptions, Inference and Learning in Graphical Models.

Why This Course

Machine learning is expected to cost $47 billion in 2020, according to Analyticsinsight.net.

A Machine Learning Engineer’s annual pay is $1,18,452. – www.glassdoor.com

monetization_on

On LinkedIn.com, Machine Learning Engineers are among the top emerging occupations.

Why Enroll In Course?

Certification in graphical models can benefit professionals in various ways. It provides comprehensive training on different types of models and helps showcase skills to potential employers, leading to increased job opportunities and higher salaries. Networking opportunities, personal and professional growth, and a competitive edge are other benefits. As the use of graphical models grows in fields such as data science, machine learning, and artificial intelligence, certification can set professionals apart from the competition.

Training Features

Live Interactive Learning

Lifetime Access

24x7 Support

Hands-On Project Based Learning

Industry Recognized Certification

Cloud

Course Curriculum

Introduction to Graphical Model

Goal: To provide a quick overview of Graphical models, graph theory, probability theory, components of graphical models, varieties of graphical models, and graphical model representation. 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

Bayesian Network

Goal: To provide a quick overview of Bayesian networks, independencies in Bayesian networks, and the construction of 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

Markov’s Networks

Goal: To provide a quick overview of Markov's networks, Markov's network independencies, the 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

Inference

Goal: To comprehend the need of inference and to interpret inference in Bayesian and Markov's networks.

Topics:

Inference
Complexity in Inference
Exact Inference
Approximate Inference
Monte Carlo Algorithm
Gibbs Sampling
Inference in Bayesian Networks

Model Learning

Goal: To comprehend the need of inference and to interpret inference in Bayesian and Markov's networks.

Topics:

Inference
Complexity in Inference
Exact Inference
Approximate Inference
Monte Carlo Algorithm
Gibbs Sampling
Inference in Bayesian Networks

Free Career Counselling

We are happy to help you 24/7

Certification

CertHippo Certification Process

CertHippo Selenium Training certificate will be issued to you once you have successfully finished your project (as reviewed by CertHippo specialists).

CertHippo certification is industry recognized, and we are the chosen training partner for many multinational corporations such as Cisco, Ford, and IBM.

Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNY Mellon, and others. Please be certain.

Online Training FAQs

How soon after Signing up would I get access to the Learning Content?

Following enrollment, you will have immediate access to the LMS and will have it for the rest of your life. You will get access to all past class recordings, PPTs, PDFs, and assignments.

Is the course material accessible to the students even after the course training is over?

Yes, once you join in the course, you will have lifetime access to the course material.

Course Description

About Graphical Models Course

Graphical Models Course is designed to teach Graphical Models, Graphical Model Fundamentals, Probabilistic Theories, Graphical Model Types – 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 Assumptions, Inference and Learning in Graphical Models.

Who should go for this training?

Researchers, Machine Learning and Artificial Intelligence enthusiasts, and anyone interested/working in the Data Science sector with a basic understanding of Machine Learning or Graphical Modeling.

What are the prerequisites for this Course?

Required Prerequisites

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

CertHippo offers you complimentary self-paced courses

Statistics and Machine learning algorithms
Python Essentials

Projects

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

A PC with an Intel i3 CPU or above, at least 3GB RAM (4GB preferred), and a 32bit or 64bit operating system are required.