- Algorithms
- Exploratory Data Analysis
- Data Analysis
- Data Manipulation
- Data Mining
- Feature Engineering
- Applied Machine Learning
- Anomaly Detection
- Unsupervised Learning
Association Rules Analysis
Completed by Anastasia Karavdina
January 19, 2024
22 hours (approximately)
Anastasia Karavdina's account is verified. Coursera certifies their successful completion of Association Rules Analysis
What you will 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 will gain
