Overview
Unsupervised learning is a type of machine learning where the model learns from data without labeled outcomes. Instead of being told what to look for, the algorithm identifies patterns and structures in the data on its own. This approach is useful for clustering similar items, reducing data dimensions, and discovering hidden insights in large datasets. Understanding unsupervised learning helps in making sense of complex data without prior knowledge.
📚 Key Learning Objectives
- ✓ Define unsupervised learning and its purpose.
- ✓ Identify key algorithms used in unsupervised learning.
- ✓ Explain the difference between supervised and unsupervised learning.
- ✓ Apply clustering techniques to group similar data points.
- ✓ Evaluate the effectiveness of unsupervised learning models.
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