Author(s): Shahidullah Kawsar

Originally published on Towards AI.

Machine Learning Interview Preparation Part 26

Anomaly detection is the practice of identifying patterns in data that deviate from expected behavior. In an era where systems generate massive volumes of real-time data, detecting anomalies early is critical for reliability, security, and performance. From spotting fraudulent transactions and network intrusions to detecting equipment failures and data quality issues, anomaly detection plays a central role across industries. Whether you work with time-series data, logs, or complex multivariate signals, understanding anomaly detection is essential for building robust, data-driven systems.

The article outlines the importance of anomaly detection across various industries, illustrating its relevance in spotting fraudulent activities, network intrusions, and operational failures. It emphasizes the need for an understanding of different types of anomalies, including point and contextual anomalies, as well as their detection methods, thus preparing readers for interviews in machine learning and data science fields.

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Published via Towards AI