In this course, we will explore anomaly detection using a range of algorithms as applicable to the data coming from IOT devices. Anomaly detection, is a complex phenomenon and there have been historically a range of techniques deployed for handling anomalies. Anomaly basically refers to a pattern in data that does not conform to a well-defined notion of a normal behavior or an observation that appears to be inconsistent with the reminder of the data set. Anomaly detection basically then refers to the problem of finding patterns in data that do not match the well-established expected behavior. This course also demonstrates a popular technique using machine learning based classification technique for anomaly detection. Also illustrated is the concept of disproportionate sampling needed for anomaly detection. Demo is provided in R.
- Understanding IOT Architecture and IOT Analytics Data Science Life Cycle.