Principal Component Analysis in Python and MATLAB

From Theory to Implementation
Rating: 4.0 out of 5 (55 ratings)
9,361 students
Principal Component Analysis in Python and MATLAB
Rating: 4.0 out of 5 (55 ratings)
9,361 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
Course content
3 sections • 9 lectures • 1h 21m total length
  • Introduction
    05:14
  • Mathematics Behind PCA – Part 1
    11:00
  • Mathematics Behind PCA – Part 2
    09:45
  • Basic PCA Implementation in Python
    14:05
  • Applying PCA to Iris Dataset Using Sciket-Learn and Python
    05:42
  • Applying PCA to Handwritten Digits Dataset in Python
    07:28
  • Basic PCA Implementation in MATLAB
    12:53
  • Applying PCA to Iris Dataset Using Statistics Toolbox of MATLAB
    07:14
  • Applying PCA to Handwritten Digits Dataset in MATLAB
    07:51

Instructors
Academic Education and Research Group
Yarpiz Team
  • 4.3 Instructor Rating
  • 2,015 Reviews
  • 43,321 Students
  • 9 Courses

The Yarpiz project is aimed to be a resource of academic and professional scientific source codes and tutorials, specially Computational Intelligence, Machine Learning, and Evolutionary Computation. Beside video tutorials, various source codes are available to download, via Yarpiz website.

The word Yarpiz (pronounced /jɑrpəz/) is an Azeri Turkish word, meaning Pennyroyal or Mentha Pulegium plant.

Programmer and Instructor
Mostapha Kalami Heris
  • 4.3 Instructor Rating
  • 2,015 Reviews
  • 43,321 Students
  • 9 Courses

Mostapha Kalami Heris was born in 1983, in Heris, Iran. He received B.S. from Tabriz University in 2006, M.S. from Ferdowsi University of Mashad in 2008, and PhD from Khaje Nasir Toosi University of Technology in 2013, all in Control and Systems Engineering.

Dr. Kalami is also co-founder of, executive officer of, and an instructor in FaraDars, an online education organization located in Iran. Also, he is a member of Yarpiz Team, which is provider of academic source codes and tutorials. He is mostly interested in the computer programming, machine learning, artificial intelligence, meta-heuristics and control engineering topics.