Association Rules Analysis

Instructor: Di Wu

Intermediate Level • 22 hours to complete 3 weeks at 7 hours a week • Flexible Schedule

What You'll Learn

  • ✓Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection
  • ✓Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.
  • ✓Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.

Skills You'll Gain

Data Mining
Machine Learning Algorithms
Data Processing
Machine Learning Methods
Algorithms
Anomaly Detection
Unsupervised Learning
Data analysis
Exploratory Data Analysis
Feature Engineering
Applied Machine Learning

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

This week provides an introduction to unsupervised learning and association rules analysis. You will explore frequent itemsets, understanding their significance in discovering patterns in transactional data. You will also explore association rules, such as support, confidence, and lift metrics as key indicators of association rule quality.

This week we will briefly discuss association rule mining, such as closed and maxed patterns.

This week focuses on the Apriori and FP Growth algorithm, a key method for efficient frequent itemset mining.

Throughout this week, you will explore the significance of outlier detection and its role in identifying unusual data points.

The final week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.