The key takeaways from this section include:

• PCA is an unsupervised learning technique which does not require labeled data
• It is also a dimensionality reduction technique which can be used to compress data and experiment with its effects on machine learning algorithms as a preprocessing step
• There are four steps to conducting 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 (in descending order of 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 scikit-learn