In this talk, we give an overview of different algebraic techniques used in the design and analysis of machine learning models. We show how we can use invariant theory to design symmetry-preserving machine learning models. We explain how Galois theory can deliver efficient, almost universal, machine learning models when the complexity of generating the ring of invariants is too large for practical purposes. And we describe how results in representation stability are related to any-dimensional machine learning models.