What is Anomaly Detection in Machine Learning?

Anomaly detection in machine learning is the process of detecting data points or observations that are different from the majority of the data points. Anomalies are data points that do not conform to the expected pattern or behavior of the data. Anomaly detection is used to uncover hidden patterns in data that can be used to detect fraud, detect system failures, identify potential threats, and more. Anomaly detection can be applied to a variety of data types, such as images, text, audio, and video. Machine learning algorithms are used to learn the patterns of normal data and then detect anomalies that deviate from the expected pattern.