Unsupervised Machine Learning
Unsupervised machine 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 that can be used to gain insights and make predictions. This is achieved through techniques such as clustering, dimensionality reduction, and anomaly detection. Unsupervised learning is particularly useful in situations where the data is complex and difficult to label, or where the goal is to discover new and unexpected patterns in the data. Examples of applications of unsupervised learning include customer segmentation, anomaly detection in network traffic, and image and speech recognition.
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