PCA (essentially SVD) the one that makes the fewest assumptions. It still works really well if your data is (locally) linear and more or less Gaussian. PLS is the regression version of PCA.

There are also nonlinear techniques. I’ve used UMAP and it’s excellent (particularly if your data approximately lies on a manifold).

https://umap-learn.readthedocs.io/en/latest/

The most general purpose deep learning dimensionality reduction technique is of course the autoencoder (easy to code in PyTorch). Unlike the above, it makes very few assumptions, but this also means you need a ton more data to train it.