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K-Means Clustering, Principal Component Analysis, and Hierarchical Clustering are examples of unsupervised learning techniques.

Supervised machine learning transforms data into real-world insights. It lets enterprises to use data to understand and prevent undesirable consequences while also increasing desired outcomes for their goal variable.

The goal of supervised learning is to create an artificial system that can learn the mapping between input and output and anticipate system output given fresh inputs.

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