Last updated on February 21, 2026 by Editorial Team
Author(s): Shahidullah Kausar
Originally published on Towards AI.
Machine Learning Interview Prep Part 27
Anomaly detection is the practice of identifying patterns in data that deviate from expected behavior. In an era where systems generate massive amounts of real-time data, early detection of anomalies is critical for reliability, security, and performance. From detecting 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, anomaly detection is essential to building robust, data-driven systems.
This article discusses various anomaly detection techniques important to different industries, emphasizing their importance in analyzing data that deviates from specific patterns. It contains multiple choice questions related to anomaly detection methods including Isolation Forest, One-Class SVM, and Autoencoders, along with explanations of the principles behind these methods. The content includes diverse scenarios that illustrate how different algorithms handle situations ranging from simple anomalies to complex collective behaviors, thus providing valuable insights for machine learning practitioners preparing for technical interviews.
Read the entire blog for free on Medium.
Published via Towards AI
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