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In this liveProject, you’ll explore a dataset with more variables and use scikit-learn and the PyOD library to build an unsupervised machine learning model for detecting cardiac arrhythmias. You’ll develop an algorithm which will detect arrhythmias from device data like EEG, using the Locally Selective Combination in Parallel Outlier Ensembles (LSCP) algorithm. A LSCP model accepts input as various other algorithms, and can be used to set up detectors with different settings.
This liveProject is for Python programmers who are interested in exploring machine learning. To begin this liveProject, you will need to be familiar with the following:
In this liveProject, you’ll master the domain of anomaly detection through exploring various methods.