Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This tutorial will guide you through the basics of unsupervised learning and its various applications.
Key Concepts
- Clustering: Grouping similar data points together. (Example: K-means clustering)
- Dimensionality Reduction: Reducing the number of variables in a dataset. (Example: Principal Component Analysis)
- Association Learning: Finding interesting relationships between variables. (Example: Apriori algorithm)
Tutorials
Here are some tutorials that will help you understand unsupervised learning better:
- Introduction to Clustering
- Dimensionality Reduction Techniques
- Association Learning in Machine Learning
Image Recognition
Unsupervised learning is also widely used in image recognition. Here's an example of an image recognition algorithm:
By using unsupervised learning, we can train algorithms to recognize patterns in images without labeled data.
Conclusion
Unsupervised learning is a powerful tool in the field of machine learning. Whether you're interested in clustering, dimensionality reduction, or association learning, there are plenty of resources available to help you learn more.