Artificial Intelligence Models and Use Cases
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
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.
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
4. Introducing Clustering
5. The K-Means Process
6. Using K-Means Clustering
7. Other Clustering Methods
8. Introducing Association Rules
9. Creating Association Rules
10. Evaluating Rules
11. Other Uses of Association Rules
Showcase Your Skills
Each time you complete a course exam, you earn a certificate that demonstrates your proficiency in that subject matter. We are proud to be able to say that Kubicle certificates are recognized by the most respected employers from around the world.
When you earn internationally-recognized certificates, you increase confidence. And when you enhance your ability, speed and accuracy, you increase your employability.