Applied Unsupervised Learning with R

Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.

Created by: Packt Publishing
Tech Knowledge in Motion

What Will I Learn?

• Implement clustering methods such as k-means, agglomerative, and divisive
• Write code in R to analyze market segmentation and consumer behavior
• Estimate distribution and probabilities of different outcomes
• Implement dimension reduction using principal component analysis
• Apply anomaly detection methods to identify fraud
• Design algorithms with R and learn how to edit or improve code

Requirements

• Although the course is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this course, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

Target audience

• Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning.