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Artificial Intelligence Models and Use Cases

Identifying Patterns

AI, when calibrated correctly, can identify trends much quicker than the human eye. In this course, you will explore unsupervised learning techniques and how they can be used to gain insights from your data.

Intermediate        11 Lessons        60 Minutes        CPD Credits
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About This Course

There are several different methods for sifting through your data using AI. In this course, you will learn how unsupervised learning techniques can be used to find clusters and associations in your dataset.

You will examine the concept of supervised learning techniques and when best to apply them. You will explore cluster analysis and how the k-means clustering algorithm works. You will also learn about association rules.

By the end of this course, you will have a broad understanding of AI learning techniques and how they can be utilized to spot patterns, trends and associations within your dataset.

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Learning Outcomes

  • Outline the purpose of unsupervised learning techniques
  • Identify use cases for unsupervised learning
  • Understand the purpose of cluster analysis
  • Explain how the k-means clustering algorithm works
  • Understand the purpose of association rules
  • Evaluate a series of association rules


1. Identifying Patterns
2. Where Can Patterns be Found
3. Introducing Your Case Study
4. Introducing Clustering
5. The K-Means Process
6. Using K-Means Clustering

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7. Other Clustering Methods
8. Introducing Association Rules
9. Creating Association Rules
10. Evaluating Rules
11. Other Uses of Association Rules
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