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.
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
On LinkedIn.com, Machine Learning Engineers are among the top emerging occupations.
Our Online Self-Learning Courses offer a self-paced approach, enabling participants to start learning at their preferred times. These courses provide structured training and incorporate review exercises to solidify understanding. You'll access instructional videos and PowerPoint presentations (PPTs) and engage in assignments, projects, and various activities aimed at improving learning outcomes, all tailored to your convenience.
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.
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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
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
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
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
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
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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.
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.
Yes, once you join in the course, you will have lifetime access to the course material.
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