Anomaly detection is an important task with many applications – right from finding outliers in the data to avoid building bad models to applications such as fraud detection. One of the main challenges with anomaly detection is the lack of labeled data to build supervised classifier models.
This video gives a brief overview of five different unsupervised techniques for Anomaly Detection. Each of these techniques is explained in more detail in subsequent videos.
