Polynomial Feature as Hidden Unit Neural Network

Hi @malvinpatrick

Yes, exactly! You can use neural networks (NNs) to solve highly nonlinear problems.
In week three, you will take a look at Transfer learning: using data from a different task which also illustrates this characteristics of NNs to model complex patterns (benefitting from prior knowledge which was incorporated in the pre-training).

Also: regarding why nonlinearity can be described well, these threads might be worth a look:

In conclusion: especially NNs w/ advanced architectures are very capable of learning very abstract and complex patterns in a highly scalable way, in particular when it comes to unstructured big data with tons of labels.

Hope that helps!

Best regards