Graphical Modelling Course | Learn Graphical Models | Edureka
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Graphical Models

Why should you take Graphical Models course ?

  • It is predicted that about $47 billion will be budgeted towards machine learning in 2020 – Analyticsinsight.net
  • The average salary for a Machine Learning Engineer is $1,18,452 – Glassdoor.com
  • Machine Learning Engineers rank among the top emerging jobs on LinkedIn.com
  • 1K + satisfied learners. Reviews
About the 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.

Instructor-led Graphical Models live online classes

02 nd  Feb
Sat & Sun (2.5 Weeks) Weekend Batch Timings : 10:00 AM - 01:00 PM (EST)

Course Price

254 299
15% OFF
    Expires in
  • 00 D
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EMI starts at 3186 / month.

    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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    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.
    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.
    Knowledge on Probability theories, statistics, Python, and Fundamentals of AI and ML.
5000 Total number of reviews
4.57 Aggregate review score
80% Course completion rate
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    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.
    Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment.

Instructor-led Sessions

15 Hours of Online Live Instructor-Led Classes. Weekend Class : 5 sessions of 3 hours each.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various Graphical Models concepts.

Assignments

Each class will be followed by practical assignment.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Certification

Towards the end of the course, Edureka certifies you as a "Graphical Models Professional" based on the project you submit.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
"You will never miss a lecture at Edureka! 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."

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover the access to our 24x7 support team will be granted instantly as well. You can start learning right away.

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

You no longer need a credit history or a credit card to purchase this course. Using ZestMoney, we allow you to complete your payment with a EMI plan that best suits you. It's a simple 3 step procedure:
  • Fill your profile: Complete your profile with Aadhaar, PAN and employment details.
  • Verify your account: Get your account verified using netbanking, ekyc or uploading documents
  • Activate your loan: Setup automatic repayment using NACH to activate your loan
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