1. Additional uses of PCA and wrap-up
Congratulations again, you have completed the exercises for principal components analysis.
2. Dimensionality reduction
During this chapter you have learned how to perform PCA in R and have gained some geometric intuition about how the algorithm works.
3. Data visualization
You have learned how PCA can be used to help visualize high dimensionality data,
4. Interpreting PCA results
how to interpret PCA models by making and reading biplots and scree plots,
5. Importance of data scaling
and how to deal with one of the most common practical issues when doing PCA: centering and scaling.
6. Up next
In the next and final chapter, you will apply everything you have learned about unsupervised learning to understanding a real-world data set.
7. Let's practice!
Let's get started!