Bayesian Networks Courses and Certifications

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
• On-Demand
• Free Video

ACM SIGPLAN

Higher Order Bayesian Networks, Exactly - POPL 2024

Explore a higher-order programming language for Bayesian networks, combining advanced techniques from linear logic, intersection types, and rewriting theory for efficient probabilistic modeling and inference.

• YouTube
• 16 minutes
• On-Demand
• Free Video

Probabilistic Graphical Models 1: Representation

18 reviews

Explore probabilistic graphical models, including Bayesian networks and Markov networks, to encode complex probability distributions for applications like medical diagnosis and speech recognition.

• Coursera
• 67 hours
• On-Demand
• Free Online Course (Audit)

Probabilistic Graphical Models 3: Learning

299 ratings at Coursera

Learn to build probabilistic graphical models from data, covering parameter estimation, structure learning, and handling incomplete data. Explore advanced techniques for both directed and undirected models.

• Coursera
• 66 hours
• On-Demand
• Free Online Course (Audit)

Bayesian Networks 3 - Maximum Likelihood - Stanford CS221: AI (Autumn 2019)

Explore maximum likelihood estimation in Bayesian networks, covering parameter learning, probabilistic inference, and advanced concepts like regularization and Expectation Maximization.

• YouTube
• 1 hour 24 minutes
• On-Demand
• Free Online Course

Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI

Explore advanced probabilistic inference techniques, including hidden Markov models, particle filtering, and Gibbs sampling, for AI applications in object tracking and more.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Online Course

Bayesian Networks 1 - Inference - Stanford CS221: AI

Explore Bayesian networks for AI, covering probabilistic inference, modeling, and applications in object tracking, language modeling, and document classification.

• YouTube
• 1 hour 22 minutes
• On-Demand
• Free Online Course

Artificial Intelligence - Principles and Techniques - Autumn 2019

Understand Artificial Intelligence, its principles and techniques with this Standford engineering course.

• YouTube
• 1 day 1 hour
• On-Demand
• Free Online Course

Learn Machine Learning algorithms, softwares, deep learning

543 ratings at Udemy

Course covering Deep learning, Neural Networks, KDD, AI, BI, ANN, Decision tree, Bayesian networks, TensorFlow n Knime

• Udemy
• On-Demand
• Free Online Course

Introduction to Artificial Intelligence

2 reviews

Explore AI's evolution from symbolic systems to machine learning, deep networks, and LLMs. Gain insights into current applications, ethical considerations, and career opportunities in this rapidly advancing field.

• Independent
• 2 weeks, 3-6 hours a week
• On-Demand
• Free Online Course

NPTEL;  IIT Delhi

An Introduction to Artificial Intelligence

Comprehensive exploration of AI fundamentals, covering search algorithms, game theory, logic, uncertainty, Bayesian networks, and reinforcement learning, with historical context and practical applications.

• YouTube
• 1 day 8 hours
• On-Demand
• Free Video

University of Tübingen

Probabilistic Machine Learning

Comprehensive lecture series on probabilistic machine learning, covering Bayesian paradigms, statistical concepts, and deep learning connections for master's-level computer science students.

• YouTube
• 1 day 16 hours
• On-Demand
• Free Online Course

Graphical Models Certification Training

Master Bayesian and Markov networks, inference techniques, and model learning for effective decision-making using graphical models.

• Edureka
• On-Demand
• Paid Course

Machine Learning and AI Foundations: Causal Inference and Modeling

136 ratings at LinkedIn Learning

Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.

• LinkedIn Learning
• 2 hours 51 minutes
• On-Demand
• Free Trial Available

Mining Data from Variable Dependencies

15 ratings at Pluralsight

This course will teach you several models like Bayesian Networks, LBP, Variable Elimination, etc. with the help of which you can derive complex relationships across multiple input variables or features.

• Pluralsight
• 1 hour 59 minutes
• On-Demand
• Free Trial Available