In the previous post, we learned about SVD and how to use SVD for low-rank approximation. Building upon the concepts of SVD now let’s learn about principal component analysis and how to use this advanced tool for machine learning problems. Imagine you are working with a dataset with many features or a higher-dimensional dataset. The first problem you will face is figuring out what these features say about the data and how to find the important features.
Additionally, It becomes really challenging to visualize such features since they exist in a higher dimension. That’s where the PCA comes in to help us. One thing you should note here since we are working with features only, not the target this makes PCA an unsupervised learning algorithm. Here are some definitions that will help you understand what it is. But before we start let’s see what you need to know before you learn about PCA.
Table of Contents
Prerequisites
- Understanding of Linear Algebra Until SVD
- Familiarity with statistical concepts such as variance, co-variance and correlation
- Python, Numpy & Scikit-Learn
What You Will Learn
- PCA Concepts
- Concept of Correlation And Covariance Matrix
- PCA Derivation & Mathematics
- PCA Application
- PCA Problems Workout – Numpy & Scikit Learn
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