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. Also the projects files are available to download at the end of this post.
The video tutorial is available to watch online, via Yarpiz YouTube Channel. The instructor of this course is Dr. Mostapha Kalami Heris, PhD of Control and Systems Engineering.
The download link of this project follows.
Principal Component Analysis Implementation in Python and MATLABDownload
Citing This Work
If you wish, you can cite this content as follows.
Cite as:Mostapha Kalami Heris, Principal Component Analysis (PCA) in Python and MATLAB — Video Tutorial (URL: https://yarpiz.com/622/yppca191211-principal-component-analysis-in-python-and-matlab-video-tutorial), Yarpiz, 2019.