What is Principal Component Analysis?
Principal Component Analysis (PCA) is a statistical procedure and an Unsupervised Learning Algorithm for reducing the dimensionality of a data set while retaining as much information as possible. PCA does this by finding a set of new variables, called “Principal Components”, that are linear combinations of the original variables. The principal components are chosen so that they are uncorrelated with each other and they explain as much of the variance in the data as possible. PCA is often used to make data sets more manageable and easier to visualize.
Why do we use PCA and why is it important to learn it?
Principal Component Analysis is useful for making data sets more manageable and easier to visualize. PCA is in some sense one of the most used unsupervised algorithms in machine learning, data mining, and statistics. It is mainly used for Dimensionality Reduction, Lossy Data Compression and Feature Extraction. PCA can be used to reduce the dimensionality of a dataset, which can make it easier to train a machine learning model or identify trends in the data.
For example, PCA can be used to reduce the number of features in a dataset of images, which can make it easier to classify the images. PCA can also be used to reduce the number of variables in a dataset of financial data, which can make it easier to identify different trends in the data.
PCA is a powerful tool for simplifying and understanding complex data sets. It is a valuable technique to learn for anyone who works with data. Here are some specific examples of how PCA can be used:
- In Image Processing, PCA can be used to reduce the number of pixels in an image without losing too much information. This can make it faster to process images and easier to store them.
- In Finance, PCA can be used to identify the most important factors that drive stock prices. This information can be used to make better investment decisions.
- In Genetics, PCA can be used to identify the most important genes that contribute to a particular disease. This information can be used to develop new treatments for the disease.
PCA is a versatile tool that can be used in a variety of fields. If you work with data, it is a valuable technique to learn.
About this Free Principal Component Analysis Course
In this free video tutorial course, we first explain what PCA is in simple terms and then review the theoretical foundations and the mathematics behind Principal Component Analysis (PCA). After that, we implement the PCA method in Python and MATLAB step-by-step. First we use Python in 3 phases and then we switch to MATLAB and do the same things there.
In the first phase, we perform the basic implementation of PCA on a randomly generated data set without using any PCA specific library and functions. In the next phase, we import the famous IRIS data set and implement PCA on it using the Scikit-Learn Library.
In the last phase, we do the same with the Handwritten Digits dataset to practice even more and gain a better understanding around how PCA works. Remember, we do all these 3 phases first in Python and then in MATLAB. The project files are available for download at the end of the post.
Whether you aim to:
- acquire the necessary skills for your first data science job,
- advance to a higher position as a software developer,
- become an expert computer scientist specializing in data science,
- or simply learn PCA to swiftly create your own projects.
this Principal Component Analysis Courses is a practical course to achieve all these goals and more.
What you will gain
After finishing this course, you will be able to:
- Explain the Theory of Principal Component Analysis (PCA)
- Describe the mathematics behind Principal Component Analysis
- Discuss why we need to use PCA and what it is exactly used for.
- Generate Random data suitable for basic PCA implementation in Python
- Plot the data and the results in the form that is suitable to analyze the results
- Apply PCA to IRIS data set in Python using Scikit-learn library
- Apply PCA to Handwritten Digits data set or any other data set in Python with Scikit-learn
- Generate random data set in MATLAB and also plot the data before and after implementing an algorithm on it.
- Implement basic PCA on randomly generated data set in MATLAB only with simple matrix calculations.
- Apply PCA to IRIS in MATLAB using Statistics and Machine Learning Toolbox
- Implement PCA on Handwritten Digits data set or any other desired data set in MATLAB with Statistics and Machine Learning Toolbox
Course Outline and Content
- Introduction to Principal Component Analysis (PCA)
- Explaining the theories and mathematics behind PCA
- Implementing PCA in Python
- Basic Implementation in Python on a randomly generated data set
- Applying PCA to IRIS data set using Scikit-learn Python library
- Applying PCA to Handwritten Digits data set using Scikit-learn Python library
- Implementing PCA in MATLAB
- Basic Implementation in MATLAB on a randomly generated data set
- Applying PCA to IRIS data set in MATLAB using Statistics and Machine Learning Toolbox
- Applying PCA to Handwritten Digits data set in MATLAB using Statistics and Machine Learning Toolbox
This course includes:
- Almost 1.5 hours on-demand video
- Access on PC, mobile, tablet and TV on different platforms such as Yarpiz website, Youtube, Udemy and Alison
- Downloadable resources
- Certificate available on Alison
Requirements
- No specific data science experience is necessary to take this course, but it is better to have some basic knowledge about data preparation and preprocessing.
- Any computer and OS will work — Windows, macOS or Linux. We will set up your code environment in the course.
- Python and MATLAB installation
What if you have questions?
In addition to its comprehensive nature, this course provides full support by addressing any inquiries you may have.
This ensures that you never find yourself stuck on a lesson for an extended period. With my guidance and support, you will progress smoothly through the course without encountering major obstacles.
Who can benefit from this course?
- Individuals seeking to initiate their learning journey in Machine Learning through PCA
- Those with an interest in Machine Learning
- Anyone aspiring to comprehend how to employ PCA in Python for dataset analysis
About the Instructor
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 Mashhad in 2008, and PhD from Khaje Nasir Toosi University of Technology in 2013, all in Control and Systems Engineering.
Dr. Kalami is a member of Yarpiz Team, which is a provider of academic source codes and tutorials. He is mostly interested in computer programming, machine learning, artificial intelligence, meta-heuristics and control engineering topics.
Watch Online
The video tutorial is available to watch online, via Yarpiz YouTube Channel. The instructor of this course is Dr. Mostapha Kalami Heris, PhD of Control and Systems Engineering.