Probabilistic Graphical Models courses can help you learn Bayesian networks, Markov random fields, and inference algorithms. You can build skills in modeling uncertainty, reasoning under uncertainty, and making predictions based on incomplete data. Many courses introduce tools like TensorFlow Probability and PyMC3, which are used for implementing these models and performing complex calculations, enabling you to apply your knowledge to real-world data analysis and machine learning tasks.

Coursera
Intermediate · Course · 1 - 4 Weeks

Duke University
★ 3.8 (798) · Intermediate · Course · 1 - 3 Months

University of Colorado Boulder
★ 4.2 (24) · Intermediate · Course · 1 - 4 Weeks

Arizona State University
Intermediate · Course · 1 - 4 Weeks

DeepLearning.AI
★ 4.7 (685) · Intermediate · Course · 1 - 4 Weeks

Duke University
★ 4.7 (395) · Intermediate · Course · 1 - 3 Months

Dartmouth College
Intermediate · Course · 1 - 3 Months

Lund University
★ 4.6 (572) · Beginner · Course · 1 - 3 Months

University of California San Diego
★ 3.6 (54) · Mixed · Course · 1 - 3 Months

University of Minnesota
★ 4.5 (65) · Beginner · Course · 1 - 4 Weeks

★ 4.7 (38) · Beginner · Course · 1 - 4 Weeks

École Polytechnique Fédérale de Lausanne
★ 4.5 (197) · Advanced · Course · 1 - 3 Months