Unsupervised Machine Learning Basics 🧠

Welcome to the world of unsupervised machine learning! This field deals with algorithms that learn from unlabelled data. Here, the machine tries to identify patterns and structures within the input data.

Common Techniques

• Clustering: Grouping data points that are more similar to each other.
• Dimensionality Reduction: Reducing the number of random variables under consideration.
• Association: Discovering relationships between variables within large datasets.

Applications

Unsupervised learning is widely used in:

• Market Basket Analysis
• Recommendation Systems
• Anomaly Detection
• Customer Segment Analysis

Unsupervised learning algorithms include:

• K-means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)

Check out more about these techniques on the Machine Learning Basics page.