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Using PCA to Improve Facial Recognition

BeginnerGuided Project

If an organization needs to process and identify individuals from a large database of images, each image may contain thousands of pixels, making it computationally expensive to compare and analyze directly. Applying PCA to these images, we can transform the pixel data into a reduced set of principal components. PCA empowers you to grasp the essence of each principal component and discover how they collectively capture the most important information present in your dataset. In this guided project, you will gain hands-on experience with PCA and learn how to apply it to solve real live problems.

4.7 (58 Reviews)

Language

  • English

Topic

  • Data Science

Enrollment Count

  • 275

Skills You Will Learn

  • Data Science, General Statistics, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • May 6, 2025
About this Guided Project

Throughout the project, you will be equipped with the tools to perform data compression, visualization, and denoising by leveraging the power of PCA.  PCA is a methodology to reduce the dimensionality of a complex problem which you will be practicing in this guided project by using it on tasks like Facial Recognition, Image Compression and Finding patterns in data of high dimension in the field of quantitative finance.

By the end of this guided project, you will have mastered the art of Principal Component Analysis and its applications. You will be equipped to reveal hidden insights, compress data, and create impactful visualizations, making you a more proficient data explorer and analyst. 

Who Should Join the Guided Project?

This guided project is tailored for data enthusiasts, analysts, and machine learning practitioners eager to unlock the potential of PCA in their data exploration journey. Participants should have a basic understanding of linear algebra, machine learning concepts, and programming fundamentals.

Instructors

Roodra Kanwar

Data Scientist at IBM

I am a data scientist by day, superhero by night. Psych! I wish I was that cool. Only the former part is true which is still pretty cool! I believe in constant learning and it is an essential part of being a productive data enthusiast. I am also pursuing my masters in computer science from Simon Fraser University specializing in Big Data. Moreover, knowledge is transfer learning (pun intended!) and what I have gained, I plan on reflecting it back to the data community.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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