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Offered By: IBMSkillsNetwork

Build an Image Retrieval System with NMF and More

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.

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Guided Project

Data Science

246 Enrolled
4.6
(43 Reviews)

At a Glance

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.


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.


Estimated Effort

30 Minutes

Level

Beginner

Skills You Will Learn

Data Science, Python

Language

English

Course Code

GPXX06S4EN

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