Anomaly Detection in Python

Overview

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

Anomalies are present in almost any dataset, and it is critical to detect and deal with them before continuing statistical exploration. This course will teach you to use Python for various anomaly detection methods. You'll visually identify outliers and apply statistical methods and techniques for univariate and multivariate data. Additionally, you'll discover how to combine multiple outlier classifiers for a reliable final estimate. Adding this skill to your existing Python repertoire will help you with data cleaning, fraud detection, and identifying system disturbances.

Syllabus

Detecting Univariate Outliers

This chapter covers techniques to detect outliers in 1-dimensional data using histograms, scatterplots, box plots, z-scores, and modified z-scores.

Isolation Forests with PyOD

In this chapter, you’ll learn the ins and outs of how the Isolation Forest algorithm works. Explore how Isolation Trees are built, the essential parameters of PyOD's IForest and how to tune them, and how to interpret the output of IForest using outlier probability scores.

Distance and Density-based Algorithms

After a tree-based outlier classifier, you will explore a class of distance and density-based detectors. KNN and Local Outlier Factor classifiers have been proven highly effective in this area, and you will learn how to use them.

Time Series Anomaly Detection and Outlier Ensembles

In this chapter, you’ll learn how to perform anomaly detection on time series datasets and make your predictions more stable and trustworthy using outlier ensembles.