Principal Component Analysis in Python and MATLAB

Principal Component Analysis in Python and MATLAB, available at Free, has an average rating of 4.6, with 9 lectures, based on 149 reviews, and has 13348 subscribers.

You will learn about 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) This course is ideal for individuals who are Data Scientists and Analysts or Computer Science and Engineering Students or Anyone interested in Data Science It is particularly useful for Data Scientists and Analysts or Computer Science and Engineering Students or Anyone interested in Data Science.

Enroll now: Principal Component Analysis in Python and MATLAB

Summary

Title: Principal Component Analysis in Python and MATLAB

Price: Free

Average Rating: 4.6

Number of Lectures: 9

Number of Published Lectures: 9

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 9

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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)

Who Should Attend

Data Scientists and Analysts
Computer Science and Engineering Students
Anyone interested in Data Science

Target Audiences

Data Scientists and Analysts
Computer Science and Engineering Students
Anyone interested in Data Science

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.

Course Curriculum

Chapter 1: Theory of Principal Component Analysis (PCA)

Lecture 1: Introduction

Lecture 2: Mathematics Behind PCA – Part 1

Lecture 3: Mathematics Behind PCA – Part 2

Chapter 2: Principal Component Analysis in Python

Lecture 1: Basic PCA Implementation in Python

Lecture 2: Applying PCA to Iris Dataset Using Sciket-Learn and Python

Lecture 3: Applying PCA to Handwritten Digits Dataset in Python

Chapter 3: Principal Component Analysis in MATLAB

Lecture 1: Basic PCA Implementation in MATLAB

Lecture 2: Applying PCA to Iris Dataset Using Statistics Toolbox of MATLAB

Lecture 3: Applying PCA to Handwritten Digits Dataset in MATLAB

Instructors

Yarpiz Team
Academic Education and Research Group
Mostapha Kalami Heris
Programmer and Instructor

Rating Distribution

1 stars: 2 votes
2 stars: 5 votes
3 stars: 23 votes
4 stars: 59 votes
5 stars: 60 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu