Udemy
UdemyPrincipal Component Analysis in Python and MATLAB
4.5(166)13k studentsALL LEVELSEnglish
Korshub's take
Overview
This course covers Principal Component Analysis (PCA), one of the most widely used unsupervised learning algorithms for dimensionality reduction, lossy data compression, and feature extraction. After reviewing the theoretical foundations, it walks through a step-by-step implementation of PCA in both Python and MATLAB.
Who It's For
Learners who want hands-on practice implementing PCA rather than just studying the theory, particularly anyone comfortable working in Python (Scikit-Learn) or MATLAB (Statistics Toolbox) who wants to see the same technique applied in both environments.
Key Takeaways
- The theoretical foundations of Principal Component Analysis
- How to implement PCA step by step using Scikit-Learn in Python
- How to implement the same PCA workflow using MATLAB's Statistics Toolbox
- Applying PCA to real datasets, including the Iris dataset and images of hand-written digits
Downloadable project files are provided, so you can follow along and re-run the exact examples covered in the course rather than rebuilding everything from scratch.
About this course
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.
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