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