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.
This course introduces you to Bayesian networks, an inductive reasoning approach ideal for situations with limited data but acce...Show More
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
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TAKEAWAY SKILLS
Content
1.
Introduction to Graphs
12 Lessons
Explore graph-based AI, focusing on Bayesian networks, Python implementation, and visualization techniques.
Introduction to the CourseGraph-Based AI ModelsWhat Are Graphs?Creating Graphs in PythonExercise: Likability in a Small TownSolution: Likeability in a Small TownPlotting Graphs in PythonDirected and Undirected GraphsDrawing AlgorithmsCyclic and Acyclic GraphsSummary, Main Concepts, and TakeawaysQuiz: Introduction to Graphs
2.
Exploring Graphs Characteristics in Python
12 Lessons
Master essential graph concepts, including centrality measures and shortest path algorithms for network analysis.
Graph CharacteristicsDegree CentralityDegree DistributionSimulate a Graph to Analyze Its CharacteristicsShortest PathExercise: Find the Shortest Path in a Small TownSolution: Find the Shortest Path in a Small TownBetweenness CentralityExercise: Calculate Betweenness Centrality in a Social NetworkSolution: Calculate Betweenness Centrality in a Social NetworkSummary, Main Concepts, and TakeawaysQuiz: Graph Characteristics
3.
Bayesian Networks
7 Lessons
Explore conditional probability, Bayes’ theorem, and Bayesian networks for informed decision-making.
4.
Graph Patterns in Bayesian Networks
12 Lessons
Explore Bayesian networks, causal relationships, and model training for informed decision-making.
5.
Structure-Based Hyperparameters in Bayesian Network
10 Lessons
Explore Bayesian networks, focusing on input nodes, synthetic nodes, and CPD optimization.
6.
Data Based Bayesian Networks
9 Lessons
Explore performance metrics, model evaluation, and parameter learning in Bayesian networks.
7.
Building a Complex Bayesian Network
6 Lessons
Master Bayesian networks through data preprocessing, modeling, and performance evaluation techniques.
8.
Evaluating the Output and Performance
6 Lessons
Master Bayesian networks for project management, enhancing decision-making, and risk assessment.
9.
Conclusion
2 Lessons
Master graph theory and Bayesian networks for advanced data analysis and decision-making.
Certificate of Completion
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