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Principal Component Analysis in Python and MATLAB
Rating: 4.5 out of 5(166 ratings)
13,711 students

Principal Component Analysis in Python and MATLAB

From Theory to Implementation
Created byYarpiz Team, Mostapha Kalami Heris
Last updated 12/2019
English

What you'll learn

  • 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)

Course content

3 sections • 9 lectures • 1h 21m total length

  • Introduction5:14
  • Mathematics Behind PCA – Part 111:00

    Form PCA as an optimization: maximize z = u^T x by a unit vector u, yielding variance z = u^T C u with C the covariance matrix.

  • Mathematics Behind PCA – Part 29:45

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

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