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Graphical Models Online Course

(4.2) 9564 Ratings
  • Enroll in Graphical Models Online Training to master probabilistic modeling and inference.
  • Learn key tools like TensorFlow, PyTorch, and Jupyter for practical implementation.
  • Gain hands-on experience with real-world data modeling projects and case studies.
  • Ideal for Data Scientists, ML Engineers, and AI Enthusiasts seeking advanced skills.
  • Choose flexible learning options: Weekday, Weekend, or Fast-Track sessions.
  • Receive career guidance, certification support, and placement assistance to succeed.

Course Duration

55+ Hrs

Live Project

3 Project

Certification Pass

Guaranteed

Training Format

Live Online (Expert Trainers)
WatchLive Classes
Guide Me Request a Call
Course fee at
₹16000

₹21000

11365+

Professionals Trained

10+

Batches every month

3162+

Placed Students

223+

Corporate Served

What You'll Learn

Learn the fundamentals of graphical models, probabilistic theory, and graph theory.

Focus on the two main types: Bayesian networks (directed graphs) and Markov networks (undirected graphs).

Learn how to perform inference to make accurate predictions and discover how to derive model structures and parameters directly from data.

Apply models to decision-making, classification, and handling uncertainty in fields like image understanding and medical diagnosis.

Learn how to build and use graphical models using software and programming languages.

The Graphical Models Online Course will cover how to use graphical models for making decisions in uncertain situations.

Graphical Models Training Overview

Industry specialists have established the Graphical Models Online Training to help you gain know-how in a key field of machine learning. You will be instructed on ideas such as Gibbs sampling, the theory of decision-making, and the theme. Bayesian methods, Markov Networks. This training allows you, under particular conditions, to learn how to make decisions. This Graphical Models Online Certification Training is meant to educate on graphical models, the basics of graphical models, probabilistic theories, types of graphical model networks, theory and assumption, concepts connected to the Bayesian and Markov networks, decision making, theory and assumption, and graphical model lessons.

Future Trends for Graphical Models

  • Integration of deep learning with probabilistic graphical models for hybrid AI systems.
  • Increased use of graphical models in explainable AI to enhance model transparency.
  • Development of scalable inference algorithms for big data and real-time analytics.
  • Expansion of graphical model applications in bioinformatics, finance, and social networks.
  • Growth of interactive and cloud-based learning platforms for hands-on model experimentation.
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Graphical Models TrainingObjectives

A career in graphical models offers excellent opportunities, particularly when combined with machine learning and data science expertise. This specialization builds a solid foundation for developing advanced AI systems and provides access to positions in analytics, research, and intelligent technology design.
Taking a graphical models online certification course can enhance your career by teaching you core machine learning skills, improving your ability to analyze and visualize data, and opening up new job opportunities in various industries.
The future of graphical models lies in their continued integration with deep learning, their expansion into complex enterprise-wide systems, and their use in cutting-edge AI applications like agent memory and real-world simulation.
Graphical models play a crucial role in machine learning by visually representing complex relationships and dependencies between variables within a dataset. This powerful approach enables efficient computation and helps model uncertainty, making it easier to analyze and predict outcomes in real-world applications.
  • Data Scientist
  • Machine Learning Engineer
  • AI / Deep Learning Researcher
  • Data Analyst
  • Students who all want to become Graphical Models entry-level jobs
  • Nodes (Variables)
  • Edges (Connections)
  • Graph Structure
  • Parameters
  • Inference Mechanism
  • Bayesian Networks
  • Markov Random Fields
  • Conditional Random Fields
  • Factor Graphs
  • Dynamic Graphical Models
With regular daily study, it typically takes around 3 to 6 months to become proficient in probabilistic graphical models. Reaching an advanced or professional level may take more time, depending on your specific goals and how much time you can dedicate to learning.
  • Medical Diagnosis
  • Natural Language Processing
  • Computer Vision
  • Robotics and Autonomous Systems
  • Weather Forecasting
Structure learning and parameter learning are the two main goals of graphical models training. The objective is to use the data at hand to create a model that faithfully captures the conditional dependencies between variables.
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Graphical Models Certification Benefits

The Graphical Models Online Certification Course provides expertise in handling complexity and uncertainty in data, which can lead to career opportunities in fields such as data science, AI, and machine learning. Students learn to represent complex, multivariate data relationships visually, create efficient algorithms, and develop skills for making decisions under uncertainty. The program also includes Graphical Models internship opportunities to help learners gain real-world Graphical Model Project experience.

  • Designation
  • Annual Salary
    Hiring Companies
  • 8L
    Min
  • 10L
    Average
  • 20L
    Max
  • 7L
    Min
  • 10L
    Average
  • 20L
    Max
  • 4L
    Min
  • 6L
    Average
  • 15L
    Max
  • 6L
    Min
  • 9L
    Average
  • 20L
    Max

About Your Graphical Models Certification Training

Our Graphical Models Online Certification Training provides a cost-effective, industry-recognized curriculum with comprehensive placement assistance. Graphical Models Online Training explains how to use a graph to describe complex problems, which is a basic aspect of machine learning. Bayesian methods, Markov networks, and inference are among the basic ideas covered in these programs. Systems are modeled using Bayesian and Markov networks. Training includes real-world case studies, tasks, and hands-on, practical experience. It also frequently provides lifetime access to learning resources.

Top Skills You Will Gain
  • Proficiency with analytical tools
  • Problem-solving
  • Model Learning
  • Expertise in Inference
  • Bayesian Networks
  • Probability & Statistics
  • Machine Learning
  • Algorithm and Data Structure

12+ Graphical Models Tools

Online Classroom Batches Preferred

Weekdays (Mon - Fri)
03 - Nov - 2025
08:00 AM (IST)
Weekdays (Mon - Fri)
05 - Nov - 2025
08:00 AM (IST)
Weekend (Sat)
08 - Nov - 2025
11:00 AM (IST)
Weekend (Sun)
09 - Nov - 2025
11:00 AM (IST)
Can't find a batch you were looking for?
₹21000 ₹16000 10% OFF Expires in

No Interest Financing start at ₹ 5000 / month

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

Why Graphical Models Course From Learnovita ? 100% Money Back Guarantee

Graphical Models Course Curriculum

Trainers Profile

Our Graphical Models Course is guided by expert trainers with extensive experience in machine learning, probabilistic reasoning, and data science. They combine academic knowledge with real-world applications, helping learners understand complex concepts through hands-on examples and practical insights. We provide comprehensive training materials covering topics like Bayesian Networks, Markov Models, and inference techniques. These resources help students enhance their analytical and modeling skills, develop intelligent systems, and advance their careers in AI, data science, and research.

Syllabus of Graphical Models Online Course Download syllabus

  • Motivation and applications of probabilistic graphical models
  • Review of probability theory and independence concepts
  • Joint, marginal, and conditional distributions
  • Graph theory basics: nodes, edges, and paths
  • Types of graphical models: directed vs. undirected
  • Factorization of joint probabilities
  • Real-world examples and use cases
  • Structure and semantics of Bayesian Networks
  • Conditional independence and d-separation
  • Factorization and joint distribution representation
  • Causal interpretation of BNs
  • Naive Bayes, Hidden Markov Models
  • Parameter estimation in BNs
  • BN visualization and querying
  • Undirected graphs and Markov properties
  • Cliques, potentials, and energy functions
  • Pairwise and higher-order MRFs
  • Factor graphs and message passing
  • Gibbs distribution and partition function
  • Ising model, image smoothing
  • Comparison of MRFs and BNs
  • Marginal and conditional probability computation
  • Variable elimination algorithm
  • Belief propagation (sum-product algorithm)
  • Max-product algorithm for MAP inference
  • Junction tree algorithm
  • Complexity and tractability of exact inference
  • Applications in small graphical models
  • Limitations of exact inference
  • Sampling-based methods (Monte Carlo, Gibbs Sampling)
  • Variational inference and Mean-Field approximation
  • Expectation Propagation overview
  • Markov Chain Monte Carlo basics
  • Evaluation of approximation quality
  • Use cases in large-scale models
  • Parameter learning (MLE, MAP estimation)
  • Structure learning from data
  • Constraint-based and score-based approaches
  • EM algorithm for incomplete data
  • Regularization and overfitting control
  • Evaluation metrics: likelihood, BIC, AIC
  • Implementation using pgmpy or pomegranate
  • Time-dependent probabilistic models
  • Hidden Markov Models structure and learning
  • Dynamic Bayesian Networks
  • Kalman filters for continuous systems
  • Particle filtering methods
  • Applications in speech, tracking, and finance
  • Visualization of temporal dependencies
  • Gaussian Graphical Models
  • Covariance and precision matrix interpretation
  • Conditional Linear Gaussian models
  • Mixed discrete-continuous variables
  • Copula-based models
  • Learning sparse GGMs (graphical lasso)
  • Applications in finance and genomics
  • Introduction to causality and structural causal models
  • Causal graphs and interventions
  • The do-calculus framework
  • Identifiability and confounding
  • Counterfactual reasoning
  • Algorithms for causal discovery
  • Deep probabilistic models (VAEs, Bayesian NNs)
  • Graph Neural Networks and Probabilistic Reasoning
  • Probabilistic programming frameworks
  • Applications in NLP, computer vision, and bioinformatics
  • Model evaluation and interpretability
  • Real-world case studies and projects
  • Final project presentation and review
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Industry Projects

Project 1
Medical Diagnosis using Bayesian Networks

Create a Bayesian model that infers potential diseases by analyzing patient symptoms, medical history, and lab data, enabling early, data-driven diagnosis with probabilistic accuracy and reduced human bias.

Project 2
Image Segmentation with Markov Random Fields

Develop an MRF-based system that segments complex images by modeling pixel dependencies, enhancing boundary detection, and texture recognition for applications in medical imaging and computer vision.

Project 3
Customer Behavior Prediction

Build a probabilistic graphical model connecting demographics, browsing habits, and purchase history to predict buying intent and deliver personalized recommendations across digital platforms.

Career Support

Our Hiring Partner

Exam & Graphical Models Certification

  • Data Scientist
  • Machine Learning Engineer
  • AI Research Analyst
  • Statistical Modeler
  • Data Analyst
Earning a Learnovita's Graphical Models certification demonstrates your expertise in understanding probabilistic relationships, dependencies, and data-driven decision-making. It validates your skills in using Bayesian and Markov models to handle uncertainty in real-world problems. With this credential, you gain a competitive edge in roles that demand strong analytical thinking, statistical modeling, and AI-based prediction capabilities.
While no certification can promise employment, this credential greatly increases your chances of landing a role in AI, machine learning, or data analytics. Employers value professionals who can model complex data patterns, and this certification highlights your ability to apply those techniques in real-world applications.
  • Basic knowledge of probability and statistics
  • Familiarity with machine learning concepts
  • Understanding of data structures and algorithms
  • Experience with Python or R programming
  • Interest in data analytics or AI modeling
This certification equips you with specialized skills in probabilistic reasoning and structured learning, enabling you to tackle complex data challenges with confidence. It not only broadens your technical expertise but also opens pathways to advanced roles in AI, research, and data science, helping you stand out in a competitive job market.

Our Student Successful Story

checkimage Regular 1:1 Mentorship From Industry Experts checkimage Live Classes checkimage Career Support
Sandeep Kumar D
Software Engineer, Cisco Systems )
It was a great learning experience. The trainer had provided relevant examples truly enjoyed the sessions, I wholeheartedly thank Training & Placement Cell for the constant support
google
review
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Vinoth P
UI Developer, TCS
I would like to appreciate the placement cell at LearnoVita for the efforts they made for providing a fair and ample amount of chances to me. boot-camps and extra classes for us to enhance our technical and interpersonal skills.
google
review
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Vijeyalakshme kannan
QA Engineer, Amazon Development Center
I feel great studying at LearnoVita and getting the great opportunity as well as support from faculties & placement officers. It is dream come true, being an IT engineer
google
review
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Pranav Srinivas
Software Testing, Capgemini
My sincere appreciation & gratitude to the Training & Placement Department and all staff of LearnoVita for their efforts in imparting quality technical and aptitude training. I am very grateful to them for effectively and sincerely helping me to grab the first-ever opportunity that came into my life.
google
review
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Prasanna
SAP Admin, HCL
I am very thankful to the faculties of LearnoVita as they are very much supportive, helpful, and the Placement Cell which is also doing a great job by arranging excellent placements of MNCs & good corporate exposure is given to students.
google
review
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How are the Graphical Models Course with LearnoVita Different?

Feature

LearnoVita

Other Institutes

Affordable Fees

Competitive Pricing With Flexible Payment Options.

Higher Graphical Models Fees With Limited Payment Options.

Live Class From ( Industry Expert)

Well Experienced Trainer From a Relevant Field With Practical Graphical Models Training

Theoretical Class With Limited Practical

Updated Syllabus

Updated and Industry-relevant Graphical Models Course Curriculum With Hands-on Learning.

Outdated Curriculum With Limited Practical Training.

Hands-on projects

Real-world Graphical Models Project With Live Case Studies and Collaboration With Companies.

Basic Projects With Limited Real-world Application.

Certification

Industry-recognized Graphical Models Certifications With Global Validity.

Basic Graphical Models Certifications With Limited Recognition.

Placement Support

Strong Placement Support With Tie-ups With Top Companies and Mock Interviews.

Basic Placement Support

Industry Partnerships

Strong Ties With Top Tech Companies for Internships and Placements

No Partnerships, Limited Opportunities

Batch Size

Small Batch Sizes for Personalized Attention.

Large Batch Sizes With Limited Individual Focus.

Additional Features

Lifetime Access to Graphical Models Course Materials, Alumni Network, and Hackathons.

No Additional Features or Perks.

Training Support

Dedicated Mentors, 24/7 Doubt Resolution, and Personalized Guidance.

Limited Mentor Support and No After-hours Assistance.

Graphical Models Course FAQ's

Certainly, you are welcome to join the demo session. However, due to our commitment to maintaining high-quality standards, we limit the number of participants in live sessions. Therefore, participation in a live class without enrollment is not feasible. If you're unable to attend, you can review our pre-recorded session featuring the same trainer. This will provide you with a comprehensive understanding of our class structure, instructor quality, and level of interaction.
All of our instructors are employed professionals in the industry who work for prestigious companies and have a minimum of 9 to 12 years of significant IT field experience. A great learning experience is provided by all of these knowledgeable people at LearnoVita.
  • LearnoVita is dedicated to assisting job seekers in seeking, connecting, and achieving success, while also ensuring employers are delighted with the ideal candidates.
  • Upon successful completion of a career course with LearnoVita, you may qualify for job placement assistance. We offer 100% placement assistance and maintain strong relationships with over 650 top MNCs.
  • Our Placement Cell aids students in securing interviews with major companies such as Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, Sports One , Infosys, MindTree, and MPhasis, among others.
  • LearnoVita has a legendary reputation for placing students, as evidenced by our Placed Students' List on our website. Last year alone, over 5400 students were placed in India and globally.
  • We conduct development sessions, including mock interviews and presentation skills training, to prepare students for challenging interview situations with confidence. With an 85% placement record, our Placement Cell continues to support you until you secure a position with a better MNC.
  • Please visit your student's portal for free access to job openings, study materials, videos, recorded sections, and top MNC interview questions.
LearnoVita Certification is awarded upon course completion and is recognized by all of the world's leading global corporations. LearnoVita are the exclusive authorized Oracle, Microsoft, Pearson Vue, and Graphical Models I exam centers, as well as an authorized partner of Graphical Models . Additionally, those who want to pass the National Authorized Certificate in a specialized IT domain can get assistance from LearnoVita's technical experts.
As part of the training program, LearnoVita provides you with the most recent, pertinent, and valuable real-world projects. Every program includes several projects that rigorously assess your knowledge, abilities, and real-world experience to ensure you are fully prepared for the workforce. Your abilities will be equivalent to six months of demanding industry experience once the tasks are completed.
At LearnoVita, participants can choose from instructor-led online training, self-paced training, classroom sessions, one-to-one training, fast-track programs, customized training, and online training options. Each mode is designed to provide flexibility and convenience to learners, allowing them to select the format that best suits their needs. With a range of training options available, participants can select the mode that aligns with their learning style, schedule, and career goals to excel in Graphical Models .
LearnoVita guarantees that you won't miss any topics or modules. You have three options to catch up: we'll reschedule classes to suit your schedule within the course duration, provide access to online class presentations and recordings, or allow you to attend the missed session in another live batch.
Please don't hesitate to reach out to us at contact@learnovita.com if you have any questions or need further clarification.
To enroll in the Graphical Models at LearnoVita, you can conveniently register through our website or visit any of our branches in India for direct assistance.
Yes, after you've enrolled, you will have lifetime access to the student portal's study materials, videos, and top MNC interview questions.
At LearnoVita, we prioritize individual attention for students, ensuring they can clarify doubts on complex topics and gain a richer understanding through interactions with instructors and peers. To facilitate this, we limit the size of each Graphical Models Service batch to 5 or 6 members.
The average annual salary for Graphical Models Professionals in India is 5 LPA to 7 LPA.
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