PCA (Principal Component Analysis) in ML | Simply Explained...
This video explains the concept of PCA(Principal Component Analysis) in the simplest way. It also includes a step-by-step method explanation using an example. Overall, the video explains the concept from the perspective of dimensionality reduction and how it can be used to solve machine learning problems. Plotting tool used: https://www.geogebra.org/m/YCZa8TAH ThinkUponAI Blog: https://thinkuponai.com/ 0:00 Daisy and her task 0:45 Why do we need PCA?? 1:40 Outline 2:30 Feature selection vs. extraction 4:50 Intuition behind eigenvectors and eigenvalues 8:34 PCA Method 9:25 Steps involved 10:07 Example 16:05 Outro
This video explains the concept of PCA(Principal Component Analysis) in the simplest way. It also includes a step-by-step method explanation using an example. Overall, the video explains the concept from the perspective of dimensionality reduction and how it can be used to solve machine learning problems. Plotting tool used: https://www.geogebra.org/m/YCZa8TAH ThinkUponAI Blog: https://thinkuponai.com/ 0:00 Daisy and her task 0:45 Why do we need PCA?? 1:40 Outline 2:30 Feature selection vs. extraction 4:50 Intuition behind eigenvectors and eigenvalues 8:34 PCA Method 9:25 Steps involved 10:07 Example 16:05 Outro