Unsupervised Learning
Unsupervised learning uses data without labels. The model explores patterns by itself. It groups data, detects structure, and reduces dimensions.
Core Idea
The model scans inputs and finds similarities. It identifies hidden groups or compressed representations. It works without labeled outputs.
How Unsupervised Learning Works
- Collect unlabeled data.
- Choose an algorithm.
- Fit the model to the data.
- Extract groups or patterns.
Main Types of Unsupervised Learning
1. Clustering
Clustering groups similar data points.
Examples
- K Means
- Hierarchical clustering
- DBSCAN
2. Dimensionality Reduction
Dimensionality reduction compresses features and keeps core structure.
Examples
- PCA
- t SNE
- UMAP
3. Association Rules
Association rules find links between items.
Examples
- Market basket analysis
- Apriori
- FP Growth
4. Anomaly Detection
Anomaly detection identifies points that do not fit normal patterns.
Examples
- Isolation forest
- One class SVM
When to Use Unsupervised Learning
- You do not have labels.
- You want to explore data structure.
- You want to group users, products, or signals.
Strengths
- No need for labels
- Reveals structure
- Supports data exploration
Limitations
- No clear accuracy metric
- Results depend on chosen algorithm
- Interpretation needs care
Common Applications
- Customer segmentation
- Anomaly detection
- Document grouping
- Feature compression
Unsupervised Learning in Moroccan Darija
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Types
- Clustering. K Means w DBSCAN.
- Dimensionality reduction. PCA.
- Association rules. Apriori.
- Anomaly detection.
Kif Kaykhddam
- Kandkhlo data bla outputs.
- Model kaydir grouping ola compression.
- Kantla3o patterns m data.
Conclusion
Unsupervised learning explores data without labels. It finds groups and hidden structure. It supports discovery tasks in many fields.