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

**(True or False)** When building a database for a Bayesian network, each row represents an instance or recorded evidence corresponding to a fixed event in the network.

(True or False) When building a database for a Bayesian network, each row represents an instance or recorded evidence corresponding to a fixed event in the network.

The random variables in the Bayesian network

The conditional dependencies between nodes

The joint probability distributions of nodes

What do the columns in a database for a Bayesian network represent?

What do the columns in a database for a Bayesian network represent?

The probability of having passed through the corresponding node, given the event or evidence

What do the cells in a database for a Bayesian network represent when modeling a transportation problem?

What do the cells in a database for a Bayesian network represent when modeling a transportation problem?

The classifier is performing no better than random chance.

What does it mean when the ROC curve is really close to the diagonal x=y?

What does it mean when the ROC curve is really close to the diagonal x=y?

The classifier is performing very poorly.

What does it mean when the ROC curve is a line y=1?


What does it mean when the ROC curve is a line y=1?

The classifier is performing no better than random chance

What does an AUC (Area under ROC curve) 0.86 indicate about a classifier’s performance?


What does an AUC (Area under ROC curve) 0.86 indicate about a classifier’s performance?

In a good classifier, how does the ROC curve behave initially?

In a good classifier, how does the ROC curve behave initially?

**(True or False)** A perfect classifier would have an AUC of 0.5.

(True or False) A perfect classifier would have an AUC of 0.5.

Test your knowledge of processing the database to build a Bayesian network, and how to use the ROC and AUC metrics.

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

Quiz: Building a Complex Bayesian Network