Principal Component Analysis
This course covers the basics of Principal Component Analysis (PCA), the need for PCA for better interpretability of large datasets by applying dimesion-reduction without any information loss.
4 hours of instruction
This course covers the basics of Principal Component Analysis (PCA), the need for PCA for better interpretability of large datasets by applying dimesion-reduction without any information loss.
OBJECTIVES
- Summarize the use-case for Principal Component Analysis
- Articulate the steps,concepts,and the relevant terminology for PCA
PREREQUISITES
Students must be comfortable using R to manipulate data and must know basic concept of machine learning.
SYLLABUS & TOPICS COVERED
- PCA
- Summarize relevant terminology for PCA
- Implement PCA and evaluate results
SOFTWARE REQUIREMENTS
You will have access to an R-based Posit Cloud environment for this course. No additional download or installation is required.
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