- 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
Online
₹ 7,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
This course offered by Edureka will enable the candidates to understand what Graphical models are, their components, types, how to represent graphical models, and decision making using them. Candidates who are interested or are currently working in the field of Data Science with basic knowledge in Machine Learning or in Graphical Modelling, Artificial Intelligence, and Machine Learning enthusiasts, and Researchers can take up this course.
Graphical Models Certification Training offered by Edureka provides the candidates with the practical and hands-on knowledge required through cloud lab on a pre-configured environment and real-life case studies which involves implementing various Graphical Models concepts. The candidates are expected to have prior knowledge of Python, Probability theories, statistics, and fundamentals of ML and AI.
The course on graphical models is made to teach in brief the Graphical Models, Probabilistic Theories, fundamentals of Graphical Models, types of Graphical Models which include Bayesian and Markov’s Networks, decision making and its assumption and theories, representation of Markov and Bayesian Networks, concepts related to Markov and Bayesian Networks, learning and inference in Graphical Models.
The highlights
- Lifetime access to LMS
- Expert Support- 24 x 7
- Real life case studies
- Online Forum
- Certification of completion will be provided by Edureka
Program offerings
- Case studies
- Assignments
- Online forum
- Live online sessions
Course and certificate fees
Fees information
The course Graphical Models Certification Training fee details is as follows:
- The course is for Rs. 7,499/-
- No cost EMI option is available for the candidates at Rs.2,500/month
Fee details for Graphical Models Certification Training
Head | Amount |
Total Fees | Rs. 7,499 + GST |
certificate availability
certificate providing authority
Eligibility criteria
Certification Qualification Details
The candidates will be eligible based on the project they submit. Hence, once the project is duly submitted and the course is completed properly, candidates will be given the certification of completion.
What you will learn
After the candidates complete the course on they will be familiar with and understand the following terms and concepts:
- Gain brief knowledge on graphical models, their components, representation, decision making using graphical models, graphical modes and types of graphical models.
- Will learn about probability theory.
- Understand the Bayesian networks, independencies in them and how to build a Bayesian network.
- Understand the Markov’s network, the independence in it, factor graphs and the network’s process.
- Will learn about the structure and parameter learning in the graphical models.
- They will understand the importance of inference and how to interpret it using Markov’s and Bayesian networks.
Who it is for
The course can be taken up by the following candidates:
- Candidates working or interested in Data science
- Candidates with basic knowledge in graphical modelling and machine learning
- Artificial Intelligence and Machine Learning enthusiasts
- Researchers
Admission details
Filling the form
The candidates who are interested to take up the course Graphical Models Certification Training will have to follow the below steps:
Step 1: Visit the course website.
Step 2: Click on Enroll on the right side of the page.
Step 3: Login in using your e-mail id and mobile number.
Step 4: After logging in the candidates can choose their desired batch for learning.
Step 5: Proceed to pay and join the course.
The syllabus
Introduction to Graphical Model
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
Markov’s networks
- 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
- Inference
- Complexity in Inference
- Exact Inference
- Approximate Inference
- Monte Carlo Algorithm
- Gibb’s Sampling
- Inference in Bayesian Networks
Model Learn
- 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
How it helps
This Graphical Models classes course will enable the candidates to understand various concepts and terms under Graphical Models. It is specifically designed to teach the fundamentals of Graphical Models, types of Graphical Models that is Bayesian which is directed and Markov’s which is undirected, Representation of both Bayesian and the Markov’s network models, probabilistic theories, concepts relating to Markov’s and Bayesian networks, learning and inference in Graphical Models, and the decision making using the theories and assumption.
This course will benefit those candidates who are currently working or are interested in the field of Data Science, researchers, artificial intelligence, and machine learning enthusiasts.
Through this Graphical Models online course the candidates will gain practical knowledge with the help of the Cloud lab access provided in this course and will also benefit from the live online sessions, case studies, the assignments, quizzes, and tests given during this course, the candidates will get 24x7 expert support and will have access to the online forum where they can interact with the faculty and their peers to clarify their doubts.
The course comes with the added benefit of if the candidates miss their scheduled live class they can either view the recorded session or sign up for the same session in the next upcoming batch.
FAQs
Yes, the candidates require knowledge in Python, statistics, probability theories, and fundamentals of ML and AI.
The candidates can view the recorded session or join the next live session batch if they miss their scheduled session.
As soon as the candidates complete the enrolment process they will gain full access to all the course contents.
The candidates will have lifetime access to the LMS after they enroll in the Graphical Models live course.
The candidates will have 24x7 lifetime access to the support team who they can call to clarify their doubts.
The candidates will be using the Cloud Lab to do their practicals and get real life experience.
Yes, the required system requirements are a system with an Intel i3 processor or above, an operating system with either 32 or 64 bit, and a minimum of 3GB RAM.
This course will improve the candidate’s employability as Machine Learning Engineers is a top emerging job on LinkedIn.
Yes, the candidates can choose their desired batch during the enrolment process.