Unsupervised Learning
Unsupervised learning is a type of machine learning where the model is trained on a dataset without any predefined labels or target variables. The goal of unsupervised learning is to identify patterns and relationships in the data, and group similar data points together. This is done through techniques such as clustering, dimensionality reduction, and anomaly detection. Unsupervised learning is often used in exploratory data analysis, where the goal is to gain insights into the data and identify potential areas of interest for further analysis. It is also used in applications such as recommendation systems, where the goal is to group similar items together based on user behavior or other characteristics.
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