Unsupervised learning is a type of machine learning where the model is trained on data without explicit labels, allowing it to identify patterns and structures inherently present in the data. It is particularly useful for tasks such as clustering, dimensionality reduction, and anomaly detection, where the goal is to explore the underlying structure of the data rather than predict a specific outcome.

Unsupervised Learning

Unsupervised learning

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

machine learning

explicit labels

identify patterns

structures inherently present

Clustering is an unsupervised learning technique used to group similar data points together based on specific characteristics or features, allowing for the discovery of patterns or structures within datasets. It is widely used in various fields such as data mining, image analysis, and market research to simplify data and make informed decisions.

clustering

Dimensionality reduction is a process used in data analysis and machine learning to reduce the number of random variables under consideration, by obtaining a set of principal variables. This technique helps in mitigating the curse of dimensionality, improving model performance, and visualizing high-dimensional data in a more comprehensible way.

dimensionality reduction

Anomaly detection is the process of identifying data points, events, or observations that deviate significantly from the expected pattern or norm in a dataset. It is crucial for applications such as fraud detection, network security, and fault detection, where identifying unusual patterns can prevent significant losses or damages.

anomaly detection

underlying structure of the data

predict a specific outcome

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