Learn how to use statistics and machine learning to detect anomalies in data. As a fundamental part of data science and AI theory, the study and application of how to identify abnormal data can be applied to supervised learning, data analytics, financial prediction, and many more industries. Understanding the theory and intuition behind these methods is an essential part of the modern developer's and researcher’s tools and knowledge base.
This course provides you with practical knowledge of the following skills:
The theory and methods used for anomaly detection from beginning to advanced levels
Derive depth-based and proximity-based detection models
Use many types of data from real-time streaming to high-dimensional abstractions
Implement these types of models using a collection of Python* labs
The course is structured around eight weeks of lectures and exercises. Each week requires approximately two hours to complete.
Get started with understanding why and how to detect anomalies in data.