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.
What you’ll learn
- Theory of Principal Component Analysis (PCA)
- Concept of Dimensionality Reduction
- Step-by-step Implementation of Simple PCA
- PCA using Scikit-Learn (Python Library for Machine Learning)
- PCA using MATLAB (Using Statistics and Machine Learning Toolbox)
This course includes
- 1.5 hours on-demand video
- Full lifetime access
- Access on mobile and TV
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