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Principal Component Analysis

Offered By: Serrano.Academy via YouTube

Tags

Data Science Courses Data Analysis Courses Machine Learning Courses Dimensionality Reduction Courses Eigenvalues Courses Eigenvectors Courses Principal Component Analysis Courses Covariance Courses

Course Description

Overview

Explore the fundamental concepts of Principal Component Analysis (PCA) in this 27-minute video tutorial. Dive into variance and covariance, eigenvectors and eigenvalues, and practical applications of PCA. Learn through a visual approach with minimal formulas and abundant illustrations. Understand dimensionality reduction using housing data examples, grasp the importance of mean and variance, and delve into covariance matrices and linear transformations. Discover the significance of eigenvalues and eigenvectors in PCA, and gain insights into how this technique can be applied to real-world data analysis problems.

Syllabus

Introduction
Taking a picture
Dimensionality Reduction
Housing Data
Mean
Variance?
Covariance matrix
Linear Transformations
Eigenstuff
Eigenvalues
Eigenvectors
Principal Component Analysis PCA
Thank you!


Taught by

Serrano.Academy

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