Course Description
Data analysis often begins with an implicit assumption that all observations are valid, accurate, and trustworthy. Unsupervised learning is used in various instances to detect anomalous behavior, for example, to identify fraudulent transactions in a bank, to find defective products by manufacturers and so on. In this course, you will perform anomaly detection using various techniques, use data transformations to identify outliers, work with Mahalanobis distances, and use regression models to improve anomaly detection performance.
Requirements
  • This course assumes prior programming knowledge in R and a basic knowledge of mathematical concepts, including exponents, square roots, means, and medians.
Instructor:
Bert Gollnick
Bert Gollnick has a Diploma in Aerospace Engineering and has pursued MSc in Economics. He is also a Data Scientist and has 10 years of experience in R. He is also an online trainer for Data Science and Machine Learning.