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:
- Example of encoding the non linearity using feature crossing - #2 by Christian_Simonis
- Choice of activation function - #8 by Christian_Simonis
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
Christian