Anomaly detection is a technique used to identify unusual patterns or behaviors in data that do not conform to expected norms. This is important in various fields, such as finance, where it can help detect fraudulent transactions, or in healthcare, where it can identify abnormal patient conditions. By analyzing large datasets, algorithms can flag these anomalies for further investigation. In machine learning, anomaly detection often involves training models on normal data to understand what typical behavior looks like. Once the model is established, it can then spot outliers, which are the data points that deviate significantly from the norm, helping organizations make informed decisions and improve security.