Home AI Courses Probabilistic Graphical Models Specialization

Probabilistic Graphical Models Specialization

(0 reviews)

Acquire a novel approach to learning and reasoning in intricate fields.

Key AI Functions:Inference,Bayesian Network,Belief Propagation,Graphical Model

Description

Reviews

Alternative Tools

Description for Probabilistic Graphical Models Specialization

Learning a novel approach to comprehending and reasoning in complex fields: Graphical Models of Probability.

Probabilistic Graphical Models (PGMs): Investigate a comprehensive framework for encoding probability distributions across complex domains.

Foundational Concepts: Investigate the intersection of graph algorithms, probability theory, and machine learning to address intricate AI issues.

Diverse Applications: Utilize PGMs in a variety of disciplines, such as medical diagnosis, image comprehension, speech recognition, and natural language processing.

Level: Advanced

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by Stanford

Duration: 4 months at 10 hours a week

Schedule: Flexible

Reviews for Probabilistic Graphical Models Specialization

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Navigating Gen AI: A CEO Playbook

Computer Science and Programming: Introduction

Reinforcement Learning Specialization

Data Engineering, Big Data, and ML on GCP Specialization

Gen AI as Your Thought Partner

Alternative Tools for Probabilistic Graphical Models Specialization

Automated Reasoning with GPT Assistant API: ReAct Agents

Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.

#Artificial Intelligence (AI) #Python Programming

Operationalizing LLMs on Azure

Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.

#Artificial Intelligence (AI) #Python Programming

Statistics with Python Specialization

Modern and Practical Statistical Thinking for All. Utilize Python for statistical visualization, inference, and modeling.

#Python Programming #Statistical inference methods

Probabilistic Graphical Models 3: Learning

In addition to addressing parameter estimation and structure learning, this course covers learning probabilistic graphical models from data and contains practical programming tasks for practical use.

#algorithms #graphical model