Online Classroom Batches Preferred
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekend Regular
(Class 3hr - 3:30Hrs) / Per Session
Weekend Fasttrack
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Top Skills You Will Gain
- Conditional probability
- Bayesian inference
- Markov chains
- Python or R
- Likelihood estimation
- Posteriori estimation
- Natural language processing
- Bioinformatics
Probabilistic Graphical Models Course Key Features 100% Money Back Guarantee
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Probabilistic Graphical Models Course Curriculam
Trainers Profile
The trainers have an advanced degree (Ph.D. or Master's) in a relevant field, such as computer science, statistics, or artificial intelligence. Trainers have experience working with relevant programming languages such as Python, R, or MATLAB.
Pre-requisites
Probabilistic Graphical Models are commonly used in machine learning applications, so students should have a good understanding of machine learning algorithms and techniques.
Syllabus of Probabilistic Graphical Models Online Course Download syllabus
- Diagnosing Bias vs Variance
- Regularisation: Cost Function
- Evaluating a Hypothesis
- Regularisation: The Problem of Overfitting
- Model Selection and Train Validation Test Sets
- Regularisation and Bias Variance
- Maximum Likelihood Estimation
- Bayesian Prediction
- Maximum Likelihood Estimation for Bayesian Networks
- Bayesian Estimation
- Bayesian Estimation for Bayesian Networks
- Maximum Likelihood for Log-Linear Models
- MAP Estimation for MRFs and CRFs
- Maximum Likelihood for Conditional Random Fields
- Likelihood Scores
- Structure Learning Overview
- Learning General Graphs: Search and Decomposability
- Bayesian Scores
- BIC and Asymptotic Consistency
- Learning Tree Structured Networks
- Learning General Graphs: Heuristic Search
- Overview
- Analysis of EM Algorithm
- EM in Practice
- Expectation-Maximisation - Introduction
- Latent Variables
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Mock Interviews
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Probabilistic Graphical Models Certification Course Training Objectives
- Probabilistic graphical models are a powerful way to represent complex domains using probability distributions. They have many uses in machine learning, computer vision, natural language processing, and computational biology.
- With the help of graphical models, we can make general message-passing algorithms that use probabilistic inference in an efficient way. So, we can answer questions like "All that is p(A|C = c)?" without listing all of the possible settings for all of the model's variables. Statistic, graph theory, and computer science are all parts of graphical models.
It will be helpful if you know the following skills:
- Python.
- Introduction to machine learning and artificial intelligence.
- Information on chance and numbers.
- Data scientists or information scientists.
- The people who know important things about Machine Learning.
- The people who have something to do with exploring space.
- Fans of artificial intelligence and Machine Learning.
- Graphical Models.
- Probabilistic techniques for Graphical Models.
- Why and how to use graphic models.
- What Makes Up a Graphical Model.
- Instructions on how to use graphical models to manage risk..
- Modes of Graphics.
- Inferences from a Graphical Model.
- Theory of Choice
- Certificate that can be shared Get a Certificate when you're done.
- 100% done on the internet Start right away and learn on your own time.
- Course in the Specialization in Probabilistic Graphical Models.
- Cutoff times that can change Set the cut-off times to match your schedule.
- moved up a level.
- Graphical models and their uses in the real world.
- The making of steel.
- Getting to know handwriting.
- Diagnosing a telecommunications network.
- Seeing objects in pictures.
- Line Graphs.
- Bar Graphs.
- Histograms.
- Line Plots.
- Frequency Table.
- Circle Graph, etc.
- Graphical techniques are rapid, simple, and make sense visually. It is possible to perform calculations with little to no specific software. Another advantage is the ability to assess the model visually by observing how well the points align.
- As a Probabilistic Graphical Model guaranteed expert in different companies, you have a lot of chances to have a good job that pays well. It gives you credibility, which will help you in your career. Probabilistic Graphical Models put engineers and clients in direct contact with each other and with partners and technical administration. So the Probabilistic Graphical Models Online course is one of the best new ways to improve your career.
- Probability is extremely important in everyday life. In the weather forecast, sports and gaming strategies, purchasing or selling insurance, online shopping and online games, blood group determination, and political strategy analysis.
- Probability has always been regarded as one of the most difficult fields of mathematics, because probabilistic reasoning sometimes produce seemingly contradictory or counterintuitive outcomes. The Monty Hall paradox and the birthday problem are two examples.
Exam & Certification
- Participate and Complete One batch of Probabilistic Graphical Models Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Probabilistic Graphical Models Certification course
- Successful completion and evaluation of any one of the given projects
This is the Certification levels that was Structured under the Probabilistic Graphical Models Certification Path.
- LearnoVita Probabilistic Graphical Models Certification
- Learn About the Certification Paths.
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- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
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Software Testing, CapgeminiProbabilistic Graphical Models Course FAQ's
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- All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
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- View the class presentation and recordings that are available for online viewing.
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