# Principal Component Analysis in Python and MATLAB

From Theory to Implementation
Rating: 4.2 out of 5 (42 ratings)
7,594 students
Principal Component Analysis in Python and MATLAB
Rating: 4.2 out of 5 (42 ratings)
7,596 students
Theory of Principal Component Analysis (PCA)
Concept of Dimensionality Reduction
Step-by-step Implementation of PCA
PCA using Scikit-Learn (Python Library for Machine Learning)
PCA using MATLAB (Using Statistics and Machine Learning Toolbox)

### Requirements

• Python Programming
• MATLAB Programming
• Basics of Data Analysis
Description

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.

Who this course is for:
• Data Scientists and Analysts
• Computer Science and Engineering Students
• Anyone interested in Data Science
Curriculum
3 sections • 9 lectures • 1h 21m total length
• Introduction
• Mathematics Behind PCA – Part 1
• Mathematics Behind PCA – Part 2
• Basic PCA Implementation in Python
• Applying PCA to Iris Dataset Using Sciket-Learn and Python
• Applying PCA to Handwritten Digits Dataset in Python
• Basic PCA Implementation in MATLAB
• Applying PCA to Iris Dataset Using Statistics Toolbox of MATLAB
• Applying PCA to Handwritten Digits Dataset in MATLAB

Instructors