Singular value decomposition (SVD) has a lot of uses, here are a few examples:

Recommendation systems: SVD is great for finding latent features in large datasets, which is very useful for recommendation systems. You can decompose a user-item matrix into latent features of users and items, and make recommendations based on similarity in the latent feature space.
Dimensionality reduction: SVD is commonly used for dimensionality reduction. You can "throw away" singular values/vectors corresponding to small singular values to reduce the dimensionality of your data while minimizing information loss.
Solving linear least squares problems: You can use the pseudoinverse from SVD to solve over-determined or under-determined linear systems.
Principal component analysis: PCA can be seen as a special case of SVD, where you just keep the top k singular vectors. So SVD can be used to perform PCA.
Image compression: The singular values from decomposing an image can be thresholded to compress the image with minimal loss.
And many more! SVD is a very fundamental and useful decomposition with a lot of applications in data science and machine learning.
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