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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.

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This course includes

76 Lessons

2 Projects

71 Playgrounds

9 Challenges

12 Quizzes

Course Overview

What You'll Learn

Course Content

Course Overview

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 simu...Show More

This course introduces you to Bayesian networks, an inductive reasoning approach ideal for situations with limited data but acce...Show More

TAKEAWAY SKILLS

Data Science

Python Programming

Graph

Artificial Intelligence

Data Statistics

What You'll Learn

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

An understanding of conditional probabilities using Bayes’ theorem

Course Content

Introduction to Graphs

12 Lessons

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

12 Lessons

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.

Introduction to Conditional Probability

Causality—Bayes' Theorem

Bayesian Networks

Transform Descriptive Graphs into Bayesian Networks

Creating Your First Bayesian Network in Python

Understanding the Output of the Model

Quiz: Bayesian Networks

Graph Patterns in Bayesian Networks

12 Lessons

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

Common Cause Graphs in Python

Causal Chain Graphs in Python

Common-Effect Graph in Python

A Simple Model: From Causality to Bayesian Networks

Exercise: Create a Bayesian Network Using Simulated Data

Solution: Create a Bayesian Network Using Simulated Data

Synthetic Nodes

Hyperparameters to Train a Bayesian Network

Exercise: Create and Train a Bayesian Network

Solution: Create and Train a Bayesian Network

Summary, Main Concepts, and Takeaways

Quiz: Graph Patterns in Bayesian Networks

Structure-Based Hyperparameters in Bayesian Network

10 Lessons

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

Number of Input Nodes and Its States

Number of Synthetic Nodes

Exercise: Using Two Logic Gates in One Bayesian Network

Solution: Using Two Logic Gates in One Bayesian Network

Number of States of the Target Node

Fine-Tuning the Conditional Probability Distribution Tables

Exercise: Setting the CPD Values of a Bayesian Network

Solution: Setting the CPD Values of a Bayesian Network

Summary, Main Concepts, and Takeaways

Quiz: Structural-Based Hyperparameters in Bayesian Network

Building a Graph-Based Bayesian Network

Project

Data Based Bayesian Networks

9 Lessons

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

Performance Metrics

The Receiver Operating Characteristic Curve

AIC and BIC Curves for Bayesian Networks

Discretize Data to Feed the Bayesian Network

Algorithms to Learn Parameters

Evaluation of Learning Algorithms Using ROC Curve

Algorithm to Learn the Structure of the BN

Inference Algorithms (To Train the Graph)

Quiz: Data-Based Bayesian Networks

Building a Complex Bayesian Network

6 Lessons

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

Preprocessing Data for Creating Bayesian Networks

Exercise: Cumulative Penality Heuristic

Solution: Cumulative Penality Heuristic

Evaluating the Penalty Heuristic Using the ROC

Summary, Main Concepts, and Takeaways

Quiz: Building a Complex Bayesian Network

Create a Knowledge and Data-Based Bayesian Network

Project

10.

Evaluating the Output and Performance

6 Lessons

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

Evaluating Accuracy from the ROC Curve

Query the Bayesian Network

Influence Analysis

Create Scenarios Based on the Bayesian Network Model

Decision Taking Based on BN

Quiz: Evaluating the Output and Performance

11.

Conclusion

2 Lessons

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

Wrapping Up

What’s Next?

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