Chapter 12: Principal Component Analysis with Scikit-Learn
In this chapter, we will learn about principal component analysis (PCA), which is a core machine learning technique that reduces the dimensionality of large datasets to determine which variables can best explain strong patterns in data. We will first introduce some mathematical concepts about orthogonal matrices and bases. Then, we will explain the method and look at the scikit-learn library's implementation of PCA. Lastly, we will apply PCA to some real-world data.
In this chapter, we will cover the following topics:
- Understanding eigenvalues, eigenvectors, and orthogonal bases
- The principal component analysis approach to dimensionality reduction
- The scikit-learn implementation of PCA
- An application of PCA to real-world data
By the end of this chapter, you will have learned the intuition and mathematics behind PCA. You will also learn about the scikit-learn library's implementation...
