Course Overview

A comprehensive framework for encoding probability distributions across complicated domains is provided by probabilistic graphical models (PGMs), which are joint (multivariate) distributions over a large number of randomly variable variables that interact. These representations, which draw on ideas in machine learning, graph algorithms, probability theory, and other fields, are at the nexus of computer science and statistics.

They serve as the foundation for cutting-edge techniques in a broad range of applications, including speech recognition, picture comprehension, medical diagnostics, natural language processing, and a number of other areas. They serve as a fundamental instrument in the formulation of numerous machine-learning tasks.

The principles of probabilistic graphical models will be practised and mastered by students in this specialisation through a variety of lectures, tests, quizzes, and programming projects.

What you'll learn

• Inference
• ?Bayesian Network
• ?Belief Propagation
• ?Graphical Model

Requirements

• ?Basic knowledge of Probabilistic Graphical Models.
• ?A computer or laptop with internet connectivity.
• ?Learn about Probabilistic Graphical Models in four months.
• Flexible work schedule.

AccessTo Career Resources with Probabilistic Graphical Models Certificate Course

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Enhanced Employability:

An outstanding Probabilistic Graphical Models Certificate that increases your chances of landing the best jobs.

Competitive Advantage:

An opportunity to stand out from the competition and make a more significant impression on potential employers.

Skill Validation:

Robust validation of your Probabilistic Graphical Models skills confirms that you can complete all related tasks.

Networking Opportunities:

The Probabilistic Graphical Models Certificate course provides ample networking opportunities for participants. Learners can connect with classmates and industry professionals through cooperative exercises, group discussions, and interactive seminars.