Unsupervised Learning - Interactive Lesson

What is Unsupervised Learning?

Unsupervised learning is machine learning with unlabeled data. The algorithm discovers hidden patterns and structures in the data without being told what to look for. Common tasks include clustering (grouping similar items), dimensionality reduction, and anomaly detection. Unlike supervised learning, there are no "correct answers" provided during training.

Task Type

Number of Clusters: 3

Data Points:0

Clusters Found:0

Iterations:0

Status:Ready

Key Difference:

Supervised: "This is a cat, this is a dog" (labeled)

Unsupervised: "Find groups in this data" (unlabeled)

Common Algorithms:

K-Means: Partition data into K clusters
DBSCAN: Density-based clustering
Hierarchical: Build cluster tree
PCA: Reduce dimensions

🎯 Clustering

Group similar data points together
No predefined categories
Examples: Customer segmentation, image compression
Algorithms: K-Means, DBSCAN

🚨 Anomaly Detection

Identify unusual patterns
Find outliers in data
Examples: Fraud detection, system monitoring
Algorithms: Isolation Forest, LOF

The Unsupervised Learning Process:

Collect unlabeled data: Gather raw, untagged data
Choose algorithm: Select clustering, reduction, etc.
Set parameters: Number of clusters, distance metric
Run algorithm: Discover patterns automatically
Interpret results: Understand what was found
Validate: Check if patterns make sense

🛒 Customer Segmentation

Group customers by behavior

🔐 Fraud Detection

Identify unusual transactions

📰 Topic Modeling

Discover themes in documents