English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 27m | 290 MB
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.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.
Table of Contents
Introduction
1 Association rule mining
2 What you should know
3 Using the exercise files
4 Using GitHub Codespaces with this course
Association Rules
5 What are association rules
6 Frequent itemset generation
7 The Apriori algorithm
8 The FP-Growth algorithm
9 Evaluating association rules
10 Why and when to use association rules
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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