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Articles Home » Artificial Intelligence and Machine Learning » Machine Learning and AI Foundations: Clustering and Association

Machine Learning and AI Foundations: Clustering and Association

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


01_001.Welcome

Machine Learning and AI Foundations: Clustering and Association with Keith McCormick

3h 22m • COURSE
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.
Topics include:
  • What is unsupervised learning?
  • Cluster and distance-based measures
  • Hierarchical cluster analysis
  • K-means cluster analysis
  • Visualizing and reporting cluster solutions
  • Cluster methods for categorical variables
  • Anomaly detection
  • Association rules
  • Sequence detection
      
Course Contents
01.Introduction 02.What_Is_Cluster_Analysis 03.K-Means 04.Visualizing_and_Reporting_Cluster_Solutions 05.Cluster_Methods_for_Categorical_Variables 06.Anomaly_Detection 07.Association_Rules_and_Sequence_Detection 08.Conclusion Ex_Files_Machine_Learning_AI_Clustering

· Posted by Superadmin on May 12 2023 · 0 Comments · 173 Reads · Print
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