Association Rules Analysis
Master unsupervised learning techniques with focus on association rules and outlier detection using Python.
Master unsupervised learning techniques with focus on association rules and outlier detection using Python.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Analysis with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Implement association rule mining techniques
Apply Apriori and FP Growth algorithms
Detect outliers using statistical methods
Analyze transactional data patterns
Develop constraint-based mining solutions
Skills you'll gain
This course includes:
1.2 Hours PreRecorded video
4 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course focuses on unsupervised learning methods, specifically association rules and outlier detection. Students learn to discover patterns in transactional data through frequent itemset mining and association rule analysis. The curriculum covers essential algorithms like Apriori and FP Growth, along with practical applications in retail and fraud detection. Through hands-on case studies and interactive tutorials, participants gain expertise in implementing these techniques using Python, making them well-equipped for real-world data analysis challenges.
Frequent Itemsets
Module 1 · 3 Hours to complete
Association Rule Mining
Module 2 · 37 Minutes to complete
Apriori and FP Growth Algorithm
Module 3 · 8 Hours to complete
Outliers
Module 4 · 4 Hours to complete
Case Study
Module 5 · 5 Hours to complete
Fee Structure
Instructor
Teaching Assistant Professor
Dr. Di Wu is a Teaching Assistant Professor at the University of Colorado Boulder, specializing in data science and computer science. His primary research interests include temporal databases, the semantic web, knowledge representation, and data science, with a focus on extending the Resource Description Framework (RDF) for temporal dimensions. Before joining CU Boulder, he taught various courses such as algorithms and data structures, programming languages, and database management. Dr. Wu aims to develop an inclusive and engaging pedagogy in data science education over the next five years, emphasizing experiential learning in both in-person and online formats. He is involved in teaching courses related to data science and programming, including specializations in Python programming for data scientists.
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