Overview
Unsupervised learning is a type of machine learning where algorithms identify patterns in data without labeled outcomes. This means the system learns from the input data alone, discovering hidden structures or groupings. It is widely used for clustering, anomaly detection, and data compression. Understanding unsupervised learning helps in analyzing large datasets and extracting valuable insights without needing prior knowledge of the data's labels.
📚 Key Learning Objectives
- ✓ Define unsupervised learning and its purpose.
- ✓ Identify key techniques used in unsupervised learning.
- ✓ Differentiate between clustering and association.
- ✓ Apply unsupervised learning algorithms to real-world data.
- ✓ Evaluate the results of unsupervised learning models.
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