Advance your career in a data-driven industry by utilizing graphical AI-modeling techniques in Python to construct and optimize causal Bayesian networks.

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This course introduces you to Bayesian networks, an inductive reasoning approach ideal for situations with limited data but access to expert knowledge. Whether you’re a developer, data scientist, or AI enthusiast, mastering Bayesian networks in Python is essential to your problem-solving toolkit.

You’ll start with the fundamentals of Bayesian networks in Python to establish network criteria and interpret data. You’ll then create and optimize network structures and explore how structured information or simulated data can be transformed into actionable Bayesian networks. Next, you’ll master hyperparameter tuning, query analysis, and the best heuristic to construct Bayesian networks.

By the end of this course, you’ll have the tools to refine and apply your new skills in real-world modeling contexts. You’ll be proficient in evaluating Bayesian networks using various metrics, including ROC curve analysis, to design and interpret powerful models, making you an invaluable asset in data-driven industries.

Designing Graphical Causal Bayesian Networks in Python

Discover how the size of a CPD table in a BN impacts its computation and learn to tackle the "curse of dimensionality" by utilizing synthetic nodes.





Introduction to Graphs

Exploring Graphs Characteristics in Python

Bayesian Networks

Graph Patterns in Bayesian Networks

Structure-Based Hyperparameters in Bayesian Network

Building a Graph-Based Bayesian Network

Data Based Bayesian Networks

Building a Complex Bayesian Network

Create a Knowledge and Data-Based Bayesian Network

Evaluating the Output and Performance

Conclusion

Synthetic Nodes

The size of a CPD table