What does unsupervised learning do?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

Unsupervised learning is a type of machine learning where the algorithm is given data without labeled responses, and its goal is to discover patterns, structures, or relationships within the data.

What Unsupervised Learning Does:

1. Groups Similar Data (Clustering):

   • Identifies natural groupings in data.

   • Example: Customer segmentation based on purchasing behavior.

2. Reduces Data Dimensions (Dimensionality Reduction):

   • Simplifies large datasets by reducing the number of variables while preserving important information.

   • Example: Principal Component Analysis (PCA) for visualization.

3. Finds Hidden Patterns:

   • Detects underlying structures or anomalies in data.

   • Example: Anomaly detection in network security.

Key Algorithms:

• Clustering: k-Means, Hierarchical Clustering, DBSCAN

• Dimensionality Reduction: PCA, t-SNE, Autoencoders

• Association Rules: Apriorism, Eclat (for market basket analysis)

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What is the goal of supervised learning?

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