Machine Learning & AI Foundations: Clustering and Association

Machine Learning & AI Foundations: Clustering and Association
Machine Learning & AI Foundations: Clustering and Association
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 22m | 532 MB

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
Table of Contents

1 Welcome
2 What you should know
3 Using the exercise files
4 What is unsupervised machine learning

What Is Cluster Analysis
5 Looking at the data with a 2D scatter plot
6 Understanding hierarchical cluster analysis
7 Running hierarchical cluster analysis
8 Interpreting a dendrogram
9 Methods for measuring distance
10 What is k-nearest neighbors

11 How does k-means work
12 Which variables should be used with k-means
13 Interpreting a box plot
14 Running a k-means cluster analysis
15 Interpreting cluster analysis output
16 What does silhouette mean
17 Which cases should be used with k-means
18 Finding optimum value for k – k = 3
19 Finding optimum value for k – k = 4
20 Finding optimum value for k – k = 5
21 What the best solution

Visualizing and Reporting Cluster Solutions
22 Summarizing cluster means in a table
23 Traffic Light feature in Excel
24 Line graphs

Cluster Methods for Categorical Variables
25 Relating clusters to categories statistically
26 Relating clusters to categories visually
27 Running a multiple correspondence analysis
28 Interpreting a perceptual map
29 Using cluster analysis and decision trees together
30 A BIRCH two-step example
31 A self organizing map example

Anomaly Detection
32 The k = 1 trick
33 Anomaly detection algorithms
34 Using SOM for anomaly detection

Association Rules and Sequence Detection
35 Intro to association rules and sequence analysis
36 Running association rules
37 Some association rules terminology
38 Interpreting association rules
39 Putting association rules to use
40 Comparing clustering and association rules
41 Sequence detection

42 Next steps