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.
4.5
76 Lessons
7h
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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.
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
Learning Roadmap
Certificate of Completion
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