Machine Learning and AI Foundations: Clustering and Association
Posted by Superadmin on May 12 2023 12:58:29

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


01_002.What you should know

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


01_003.Using the exercise files

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


01_004. What_is_unsupervised_machine_learning

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


02_005. Looking_at_the_data_with_a_2D_scatter_plot

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


02_006. Understanding_hierarchical_cluster_analysis

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


02_007. Running_hierarchical_cluster_analysis

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


02_008. Interpreting_a_dendrogram

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


02_009. Methods_for_measuring_distance

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


02_010. What_is_k-nearest_neighbors

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_011. How_does_k-means_work

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_012. Which_variables_should_be_used_with_k-means

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_013. Interpreting_a_box_plot

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_014. Running_a_k-means_cluster_analysis

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_015. Interpreting_cluster_analysis_output

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_016. What_does_silhouette_mean

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_017. Which_cases_should_be_used_with_k-means

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_018. Finding_optimum_value_for_k_-_k_=_3

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_019. Finding_optimum_value_for_k_-_k_=_4

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_020. Finding_optimum_value_for_k_-_k_=_5

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


03_021. What_the_best_solution

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


04_022. Summarizing_cluster_means_in_a_table

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


04_023. Traffic_Light_feature_in_Excel

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


04_024. Line_graphs

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


05_025. Relating_clusters_to_categories_statistically

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


05_026. Relating_clusters_to_categories_visually<

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


05_027. Running_a_multiple_correspondence_analysis

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


05_028. Interpreting_a_perceptual_map

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


05_029. Using_cluster_analysis_and_decision_trees_together

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


05_030. A_BIRCH_two-step_example

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


05_031. A_self_organizing_map_example

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


06_032. The_k_=_1_trick

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


06_033. Anomaly_detection_algorithms

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


06_034. Using_SOM_for_anomaly_detection

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


07_035. Intro_to_association_rules_and_sequence_analysis

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


07_036. Running_association_rules

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


07_037. Some_association_rules_terminology

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


07_038. Interpreting_association_rules

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


07_039. Putting_association_rules_to_use

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


07_040. Comparing_clustering_and_association_rules

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


07_041. Sequence_detection

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


08_042. Next_steps

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

Machine Learning and AI Foundations: Clustering and Association

Created by Keith McCormick


Ex_Files_Machine_Learning_AI_Clustering.zip

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