Transcription
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 within the data, and group similar data points together. This type of learning is useful when the data is unstructured and there is no clear way to label it. Unsupervised learning algorithms include clustering, dimensionality reduction, and anomaly detection. Clustering algorithms group similar data points together based on their similarity, while dimensionality reduction techniques reduce the number of features in the data while preserving the important information. Anomaly detection algorithms identify data points that are significantly different from the rest of the data. Unsupervised learning is widely used in applications such as customer segmentation, image and speech recognition, and anomaly detection in cybersecurity.
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