Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Coursera Probabilistic Graphical Models 3: Learning certification course covers probabilistic graphical models, commonly known as PGMs. They are a rich framework used for encoding probability distributions over complex domains. They form the foundation of state-of-the-art methods in different applications such as speech recognition, natural language processing, medical diagnosis, image understanding, and many more. They are also instrumental in formulating many machine learning problems.
The Probabilistic Graphical Models 3: Learning training is the third course in a series of three. Where the first course focused on representation, and the second focused on inference and the final course helps in addressing questions related to learning. The course discusses the main problems of parameter estimation in both directed and undirected models.
Moreover, students can receive a course completion certificate for the Coursera Probabilistic Graphical Models 3: Learning programme. Candidates can attach the certificate to their LinkedIn profile or their resume/CV.
The Highlights
- Advanced-level course
- Flexible deadlines
- Online training
- Course delivery in English with multilingual subtitles
- Self-paced education
- A Stanford University offering
- Approximately 66 hours to complete
- Financial aid available
- Practice quizzes
- Shareable certificate
- Free enrolment
Programme Offerings
- Online Course
- financial aid
- Shareable Certificate
- Flexible Deadlines
- video transcript
- Practise Exercises
- self-directed training
- Pre-recorded Video Lectures
- 66 Hours For Completion
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | StanfordCoursera |
Probabilistic Graphical Models 3: Learning Fee Structure:
| Subscription | Amount |
| 1 Month | ₹1,676 |
| 3 Month | ₹5,029 |
| 6 Month | ₹25,146 |
Eligibility Criteria
To get the Probabilistic Graphical Models 3: Learning certification by Coursera, candidates must watch all the video lectures, read the study material and also complete all the practise exercises. Candidates also need to get the minimum passing marks. On fulfilling all the specifications, candidates will get a course completion certificate from Coursera, which can be attached to their LinkedIn page or to their resume/CV.
What you will learn
By the end of the Probabilistic Graphical Models 3: Learning certification syllabus, candidates should be able to:
- Implement MAP parameter estimation and maximum likelihood for Markov networks
- Implement both Bayesian parameter estimation and maximum likelihood for Bayesian networks
- Implement the Expectation-Maximisation (EM) algorithm for a Bayesian network
- For a given situation, evaluate which scoring function is appropriate
- Formulate a structure learning problem as a combinatorial optimisation task, over network structure
- Utilise PGM inference algorithms so as to support more effective parameter estimation for PGMs
Who it is for
The people who want to be ML Engineers can without any doubt pursue this Coursera programme:
Admission Details
Candidates planning to pursue Coursera Probabilistic Graphical Models 3: Learning classes online programme can follow the steps given below to apply:
- Go to https://www.coursera.org/
- In Coursera’s programme catalogue, search for “Probabilistic Graphical Models 3: Learning” and open the course information page.
- Choose the “Enroll for Free” option.
- Existing account holders can log in and enrol for the course. New users need to create an account or use Google, Facebook, or Apple ID to log in.
Application Details
Candidates are not required to fill out a separate application form to join the Probabilistic Graphical Models 3: Learning programme by Coursera. Sign up with Coursera using their Google, Facebook, or Apple account to get access to the learning material for free. Besides, the students who desire a certificate for the course can purchase the certification experience.
The Syllabus
Video
- Learning: Overview
Video
- Regularization: The Problem of Overfitting
- Regularization: Cost Function
- Evaluating a Hypothesis
- Model Selection and Train Validation Test Sets
- Diagnosing Bias vs Variance
- Regularisation and Bias Variance
Videos
- Maximum Likelihood Estimation
- Maximum Likelihood Estimation for Bayesian Networks
- Bayesian Estimation
- Bayesian Prediction
- Bayesian Estimation for Bayesian Networks
Assignments
- Learning in Parametric Models
- Bayesian Priors for BNs
Videos
- Maximum Likelihood for Log-Linear Models
- Maximum Likelihood for Conditional Random Fields
- MAP Estimation for MRFs and CRFs
Assignment
- Parameter Estimation in MNs
Programming Assignment
- CRF Learning for OCR
Videos
- Structure Learning Overview
- Likelihood Scores
- BIC and Asymptotic Consistency
- Bayesian Scores
- Learning Tree Structured Networks
- Learning General Graphs: Heuristic Search
- Learning General Graphs: Search and Decomposability
Assignments
- Structure Scores
- Tree Learning and Hill Climbing
Programming Assignment
- Learning Tree-structured Networks
Videos
- Learning With Incomplete Data - Overview
- Expectation Maximization - Intro
- Analysis of EM Algorithm
- EM in Practice
- Latent Variables
Assignments
- Learning with Incomplete Data
- Expectation Maximization
Programming Assignment
- Learning with Incomplete Data
Video
Summary: Learning
Assignment
- Learning: Final Exam
Video
- PGM Course Summary