Probabilistic Models Courses and Certifications

Probabilistic Graphical Models 1: Representation

18 ratings

Encode complex joint distributions as graphs: build Bayesian and Markov networks, template models like HMMs and plate models, structured CPDs, and influence diagrams for decisions.

• Coursera
• 7 weeks, 10 hours a week
• Self-Paced
• Paid Course

Probabilistic AI School

Introduction to Probabilistic Models - Probabilistic AI School

Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.

• YouTube
• 2 hours 10 minutes
• Self-Paced
• Free Video

Probabilistic AI School

Introduction to Probabilistic Models - Lecture 1

Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.

• YouTube
• 2 hours 11 minutes
• Self-Paced
• Free Video

Career Certificate

Probabilistic Graphical Models

1552 ratings at Coursera

Encode joint distributions over many interacting random variables: learn graphical representation, inference algorithms, and parameter and structure learning for probabilistic graphical models.

• Coursera
• 16 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 3 courses

Fundamentals of Probabilistic Graphical Models

Learn to model probability distributions with Bayes Nets and apply Hidden Markov Models to sequence tasks such as spam classification and part-of-speech tagging.

• Udacity
• 14 hours
• Self-Paced
• Paid Course

Probabilistic Graphical Models 2: Inference

491 ratings at Coursera

Answer queries with probabilistic graphical models: variable elimination, clique tree belief propagation, MAP message passing, and sampling methods like Gibbs and Metropolis-Hastings.

• Coursera
• 7 weeks, 5 hours a week
• Self-Paced
• Paid Course

Probabilistic Graphical Models 3: Learning

305 ratings at Coursera

Learn how graphical models are learned from data: maximum likelihood and Bayesian parameter estimation, undirected model training, Bayesian network structure search, and EM for incomplete data.

• Coursera
• 8 weeks, 8 hours a week
• Self-Paced
• Paid Course

Natural Language Processing with Probabilistic Models

1790 ratings at Coursera

This is the Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai

• Coursera
• 4 weeks, 8 hours a week
• Self-Paced
• Paid Course

Wolfram

The BayesianNetwork Paclet - Graphical Probabilistic Models for Reasoning Under Uncertainty

Explore Bayesian networks for probabilistic modeling and uncertainty reasoning using Wolfram's specialized paclet for multivariate probability distributions.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

UofU Data Science

Probabilistic Graphical Models and Mean Field Inference - Lecture 20

Dive into advanced data science concepts including local version inference, alternating updates, and mean field version updates for practical applications in graphic models and word distribution.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Coursera

Probabilistic Graphical Models: A Compact Introduction

Build Bayesian and Markov network representations from conditional independence, run variable elimination and belief propagation, and weigh exact versus sampling inference in Python with pgmpy and pomegranate.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Practical Methodology and Ethics in AI

11 ratings at Coursera

Train, troubleshoot, and deploy deep learning models while addressing bias and fairness, and integrate structured probabilistic models to handle uncertainty and incomplete data.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Data Science Courses

Denoising Diffusion Probabilistic Models and Generative Moment Matching Networks - Lecture 17

Delve into diffusion models and DDPMs, exploring progressive data refinement techniques and generative moment matching networks using Maximum Mean Discrepancy for distribution analysis.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

UofU Data Science

Probabilistic Graphical Models and Maximum Likelihood Estimation - Lecture 13

Dive into probabilistic graphical models, exploring likelihood estimation, Gaussian models, and network structures while learning to analyze real-world data using conditional independence concepts.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Stanford CS236 - Deep Generative Models - Lecture 1: Introduction

Explore deep generative models with Stanford's Prof. Ermon. Learn key concepts, applications, and cutting-edge techniques in this comprehensive introduction to a rapidly evolving field of AI.

• YouTube
• 57 minutes
• Self-Paced
• Free Video