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Unsupervised learning aims to discover patterns in unlabeled data from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Unsupervised learning is a type of machine learning that deals with unlabeled data, meaning that we don't have the correct output associated with each data point. Instead, the goal is to discover patterns or relationships in the data without any guidance. This is in contrast to supervised learning, where the algorithm is given labeled examples to learn from. In unsupervised learning, the algorithm must find structure in the data on its own. One common task in unsupervised learning is clustering, where the algorithm groups similar data points together. This can help us understand the underlying structure of the data and discover natural groupings that we may not have been aware of. Another task is dimensionality reduction, where the algorithm tries to represent the data in a lower-dimensional space while preserving important features. By uncovering patterns in the data, unsupervised learning can help us gain insights into the underlying relationships between data points. This can be useful in a variety of applications, such as customer segmentation for targeted marketing, anomaly detection for fraud prevention, or even just exploring and understanding the structure of the data itself.- Unsupervised learning is a powerful tool for discovering hidden patterns in data without the need for labeled examples. It allows us to explore the structure of the data and gain insights that may not be apparent at first glance. By leveraging unsupervised learning techniques, we can uncover valuable knowledge and make better-informed decisions in various domains.
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