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
Skills you'll gain: LangGraph, AI Orchestration, LangChain, AI Workflows, LLM Application, Agentic Workflows, Agentic systems, Tool Calling, Generative AI Agents, Model Deployment, Large Language Modeling, Artificial Intelligence, Context Management, Data Persistence, Business Logic, Enterprise Architecture, Debugging
Intermediate · Course · 1 - 4 Weeks