Intermediate Outlier Detection

4 hours of instruction

Detecting outlier data points are powerful machine learning techniques. This course covers how techniques like Local Outlier Factor and Isolation Forest play a role in anomaly and outlier detection. By the end of the course, students will learn to implement these techniques to identify anomalous data points.

OBJECTIVES

1. Understand the concepts and working of specific outlier detection algorithms like LOF and Isolation Forest
2. Implement and optimize these models to identify anomalies in a dataset

PREREQUISITES

Introduction to Outlier Detection

SYLLABUS & TOPICS COVERED

1. LOF
   • Local Outlier Factor algorithm use cases and logic behind it
   • Implement and optimize LOF on a dataset
2. Isolation Forest
   • Isolation forest algorithm use cases and logic behind it
   • Implement and optimize Isolation Forest on a dataset

SOFTWARE REQUIREMENTS

You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.