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Businesses face an ever-growing difficulty in a world where data pours like a digital river: how to discern the usual from the anomalous, the ordinary from the extraordinary. This is where the strong answer to an old issue comes in: machine learning for anomaly detection. Anomalies, which are frequently referred to as outliers or inconsistencies in the data, are essential to quality control, anomaly detection, and data analysis. Anomaly detection has applications in many domains, such as finance (fraudulent transaction detection), healthcare (early disease detection), manufacturing (equipment failure prediction), and cybersecurity (unusual network activity recognition). Nowadays, companies of all sizes…
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