In this lesson, you'll get a brief recap of key concepts from this section.
You will be able to:
- Highlight key takeaways concerning PCA
- Recall that PCA is a dimensionality reduction technique
- There are four steps to calculating PCA:
- Center each feature by subtracting the feature mean
- Calculate the covariance matrix for your normalized dataset
- Calculate the eigenvectors/eigenvalues for the covariance matrix
- Reorder your eigenvectors based on their accompanying eigenvalues (descending order to the eigenvalues)
- Take the dot product of the transpose of the eigenvectors with the transpose of the normalized data
- You can also easily implement PCA using sci-kit learn
PCA is an essential tool in your skill set. Use it when you need to compress data, and experiment with its effect on machine learning algorithms as a preprocessing step. With PCA, you've now been exposed to unsupervised learning techniques which do not require labeled data.