What’s Next?

Explore where you can apply the knowledge of Bayesian networks to solve real-world problems.

Congratulations on completing this comprehensive course on graph-based AI and Bayesian networks! Your dedication and efforts in traversing this complex and exciting realm of AI have undoubtedly equipped you with a powerful set of tools that you can apply in real-world situations.

Thank you for your commitment to learning. We hope this course has sparked your curiosity and driven your enthusiasm to explore the field of AI further.

You're now not only well-equipped to create, optimize, and evaluate complex Bayesian network models, but you are also ready to apply your knowledge to real-world problems using graph-based AI models.

In each part of the course, we provided plenty of examples, quizzes, and exercises to solidify your understanding. This practice-oriented approach ensures that you are ready to apply your skills to various situations in data science, becoming a confident practitioner of graph-based AI.

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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

What’s Next?