Grasp the essentials of dimensionality reduction and lay the groundwork for your journey by understanding and implementing Principal Component Analysis (PCA) using Python's Scikit-learn. This launchpad course provides a comprehensive introduction into why, how and when to use PCA for feature extraction and enhancing computational efficiency in high-dimensional data sets.
Overview
Syllabus
- Lesson 1: Practical Guide to Principal Component Analysis (PCA) in Data Science
- Lesson 2: Mastering PCA: Eigenvectors, Eigenvalues, and Covariance Matrix Explained
- Lesson 3: Mastering Principal Component Analysis with Scikit-learn
- Lesson 4: Mastering PCA: Interpretation and Application in Machine Learning
