Get started with understanding why and how to detect anomalies in data.
- Define various types of anomalies
- Discuss the applications of anomaly detection
- Explain the statistics and mathematics required
Learn how to build upon probability theory and geometry to identify anomalies.
- Describe probabilistic models for anomaly detection
- Apply extreme value analysis and angle-based techniques
- Use Python to perform anomaly detection on one- and two-dimensional data
See how to use linear models instead of probabilistic and geometric models.
- Apply linear regression models and principal component analysis
- Use support vectors machines (SVMs) for anomaly detection
Explore how to use additional methods based on distance to identify abnormal data.
- Describe proximity-based methods and the local outlier factor (LOF)
- Apply the k-nearest neighbors (KNN) algorithm and k-means clustering
Learn how to work with difficult problems that involve high-dimensional data.
- Understand the difficulties with high-dimensional problems
- Apply the subspace method with feature bagging and the isolation forest algorithm