Machine Learning and AI Foundations: Clustering and Association

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Created by: Keith McCormick

Produced in 2018

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Course Description

Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning techniques- "cluster analysis, anomaly detection, and association rules- "to get accurate, meaningful results from big data.

Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses the same algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler. He closes the course with a review of association rules and sequence detection, and also provides some resources for learning more.

All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software.

Intermediate Level

Time: 3h 22m

Instructor Details

Keith McCormick

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Keith McCormick is an independent data miner, trainer, speaker, and author.

Keith is skilled at explaining complex methods to new users or decision makers at many levels of technical detail. He specializes in predictive models and segmentation analysis including classification trees, neural nets, general linear model, cluster analysis, and association rules.