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Build an Image Retrieval System with NMF and More

BeginnerGuided Project

How would you create an image retrieval system to find similar images? Non-Negative Matrix Factorization would be the right tool to use. It is an unsupervised learning technique used for decomposing data. Non-Negative Matrix Factorization will be a useful and powerful tool since factorized matrices can be interpreted as real images. Check out this guided project to find out what NMF is, how it works and how to apply it to solve real life business problems.

4.6 (50 Reviews)

Language

  • English

Topic

  • Data Science

Enrollment Count

  • 261

Skills You Will Learn

  • Data Science, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • May 10, 2025
About this Guided Project

Throughout this project, you will gain an understanding of NMF's theoretical concepts and practical implementation. We will apply NMF to real-world datasets to extract meaningful patterns and components. By the end of this project, you will be familiar with NMF's uses and how it can be applied to different domains.


Who Should Participate?

This guided project is suitable for data enthusiasts, machine learning practitioners, and individuals interested in Non-Negative Matrix Decomposition. Participants should have a basic understanding of linear algebra and Python programming fundamentals. No prior experience with NMF is required, as we will cover the necessary theoretical foundations and practical implementations.


Instructors

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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Roxanne Li

Data Scientist at IBM

I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!

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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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