Machine Learning with Python: Association Rules
1h 27mIntermediate2022-11-09
Authors
Frederick Nwanganga
Information Technology Professional and Teacher
Course details
Join instructor Frederick Nwanganga as he introduces a practical, easy-to-understand approach to using machine learning to solve real-world problems and provides step-by-step guidance on how to do this in Python. Frederick focuses specifically on association rules and how you can apply them for market basket analysis. He explains what association rules are and goes over two popular algorithms, then dives into when and why you should use association rules. Plus, Frederick covers how to create, visualize, and interpret association rules in Python.
Skills covered
Machine LearningPythonArtificial Intelligence (AI)Programming LanguagesOpen SourceSoftware DevelopmentDeep Dive (X:Y)
Concepts
0. Introduction
- 01 - Association rule mining
- 02 - What you should know
- 03 - Using the exercise files
- 04 - Using GitHub Codespaces with this course
1. Association Rules
- 05 - What are association rules
- 06 - Frequent itemset generation
- 07 - The Apriori algorithm
- 08 - The FP-Growth algorithm
- 09 - Evaluating association rules
- 10 - Why and when to use association rules
2. Discovering Patterns with Association Rules
- 11 - How to collect data for association rule mining
- 12 - How to generate frequent itemsets
- 13 - How to create association rules
- 14 - How to evaluate association rules
Conclusion
- 15 - Next steps
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