Unsupervised learning is a type of machine learning where the algorithm is left to find patterns and insights from the data without any labels. This section aims to explore the various aspects of unsupervised learning and its applications.
Key Concepts
- Clustering: Grouping similar data points together.
- Dimensionality Reduction: Reducing the number of variables in the dataset.
- Anomaly Detection: Identifying unusual patterns that do not conform to the normal behavior.
Applications
Unsupervised learning has a wide range of applications, including:
- Market Basket Analysis: Understanding customer buying patterns.
- Image Compression: Reducing the size of images while maintaining quality.
- Recommendation Systems: Suggesting items that a user might like.
Resources
For further reading, check out our Introduction to Unsupervised Learning.
Images
Clustering Example
Dimensionality Reduction Example
Anomaly Detection Example