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Designing Graphical Causal Bayesian Networks in Python

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

76 Lessons

3 Projects

Join 2.9 million developers at

LEARNING OBJECTIVES

An understanding of conditional probabilities using Bayes’ theorem
Familiarity with representing network structures using Python’s NetworkX library
Hands-on experience applying evaluation methods like degree and betweenness centrality for graph node significance assessment
The ability to build Bayesian networks using Python’s CausalNex library
Working knowledge of query analysis and data interpretation
Proficiency in assessing Bayesian Network performance with ROC curve analysis and essential metrics

Learning Roadmap

76 Lessons2 Projects12 Quizzes9 Challenges

Introduction to Graphs

Explore graph-based AI, focusing on Bayesian networks, Python implementation, and visualization techniques.

Introduction to the Course

Graph-Based AI Models

What Are Graphs?

Creating Graphs in Python

Exercise: Likability in a Small Town

Solution: Likeability in a Small Town

Plotting Graphs in Python

Directed and Undirected Graphs

Drawing Algorithms

Cyclic and Acyclic Graphs

Summary, Main Concepts, and Takeaways

Quiz: Introduction to Graphs

Exploring Graphs Characteristics in Python

Master essential graph concepts, including centrality measures and shortest path algorithms for network analysis.

Graph Characteristics

Degree Centrality

Degree Distribution

Simulate a Graph to Analyze Its Characteristics

Shortest Path

Exercise: Find the Shortest Path in a Small Town

Solution: Find the Shortest Path in a Small Town

Betweenness Centrality

Exercise: Calculate Betweenness Centrality in a Social Network

Solution: Calculate Betweenness Centrality in a Social Network

Summary, Main Concepts, and Takeaways

Quiz: Graph Characteristics

Bayesian Networks

7 Lessons

Explore conditional probability, Bayes’ theorem, and Bayesian networks for informed decision-making.

Overcoming Uncertainty with Bayesian Probability in Python

Project

Premium

Graph Patterns in Bayesian Networks

12 Lessons

Explore Bayesian networks, causal relationships, and model training for informed decision-making.

Structure-Based Hyperparameters in Bayesian Network

10 Lessons

Explore Bayesian networks, focusing on input nodes, synthetic nodes, and CPD optimization.

Building a Graph-Based Bayesian Network

Project

Data Based Bayesian Networks

9 Lessons

Explore performance metrics, model evaluation, and parameter learning in Bayesian networks.

Building a Complex Bayesian Network

6 Lessons

Master Bayesian networks through data preprocessing, modeling, and performance evaluation techniques.

Create a Knowledge and Data-Based Bayesian Network

Project

Evaluating the Output and Performance

6 Lessons

Master Bayesian networks for project management, enhancing decision-making, and risk assessment.

Conclusion

2 Lessons

Master graph theory and Bayesian networks for advanced data analysis and decision-making.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

ABOUT THIS COURSE

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.

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