Principal Component Analysis (PCA) in Python and MATLAB

Principal Component Analysis (PCA) is an unsupervised learning algorithms and it is mainly used for dimensionality reduction, lossy data compression and feature extraction. It is the mostly used unsupervised learning algorithm in the field of Machine Learning.

In this video tutorial, after reviewing the theoretical foundations of Principal Component Analysis (PCA), this method is implemented step-by-step in Python and MATLAB. Also, PCA is performed on Iris Dataset and images of hand-written numerical digits, using Scikit-Learn (Python library for Machine Learning) and Statistics Toolbox of MATLAB. For more information and download the video and project files and lecture notes for this tutorial, see: https://yarpiz.com/yppca191211

Publisher: Yarpiz (https://www.yarpiz.com/)
Instructor: Mostapha Kalami Heris
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Видео Principal Component Analysis (PCA) in Python and MATLAB канала Yarpiz