In this course, you will :
- Learn how the algorithm works under the hood, how to implement k-means clustering in R, how to visualise and interpret the results, and how to choose the number of clusters when it is unknown in advance.
- Learn what PCA is, how to visualise the results of PCA with biplots and scree plots, and how to deal with practical issues like data centering and scaling before performing PCA.
- use the unsupervised learning techniques covered in the first three chapters to guide you through a complete analysis
- Unsupervised learning in R
- Hierarchical clustering
- Dimensionality reduction with PCA
- Putting it all together with a case study