Implementing Anomaly Detection in Python

The cover image for anomaly detection course by Bex Tuychiev

Check out the course.

Overview

A comprehensive exploration of anomaly detection techniques using Python, focusing on identifying unusual patterns in data that don’t conform to expected behavior. This course enhanced my expertise in detecting fraud, system failures, and other irregular events in large datasets.

Key Skills Demonstrated

  • Statistical methods for anomaly detection (Z-score, IQR, Mean Absolute Deviation)
  • Machine learning approaches (Isolation Forest, LOF, KNN)
  • Time series anomaly detection
  • Feature engineering for anomaly detection
  • Python libraries: scikit-learn,numpy,pandas,scipy,pyod

Course Impact & Applications

  • Enabled students to build fraud detection systems using Python and statistical methods
  • Taught practical implementation of anomaly detection for system monitoring
  • Provided hands-on experience with machine learning models for outlier detection
  • Equipped learners with skills to analyze and visualize anomalous patterns in data

Tools & Technologies

  • Python (scikit-learn, pandas, numpy)
  • Statistical analysis tools (scipy)
  • Machine learning frameworks (pyod)
  • Data Visualization (matplotlib, seaborn)