Principal Component Analysis
Author: Eleena Barska
Principal component analysis is one of the most popular data dimensionality reduction algorithms. This course will help you understand how to create PCA models and analyze the results. Let's start!
Complete all chapters to get certificate
What is Principal Component Analysis
Let's take a look at PCA (Principal Component Analysis) from afar. In this section, you will learn what PCA is, why this data processing method is needed and see the real problems it solves.
Practical Application of PCA
Examples of Real Problems
How to Explain the Obtained Results?
Basic Concepts of PCA
Learn about the design of the PCA algorithm. This section will show each stage in detail and simply.
Eigenvalues and Eigenvectors
Feature Vector and Principal Components
Seeing the Big Picture
Set up your first PCA model using the Python library and solve the challenge provided for you.
Scikit-learn for PCA
Fit Data into the Model
It's time to find out how we can explain the results. What do the first, second and third components tell us? What can we use the data for in the future?
Explain Resulting Components