The kernel method has become one of the standard tools for solving lattice path enumeration problems. I will start by introducing the method in the context of constrained 1-dimensional random walks and simple polymer models. When we move up to 2 dimensions, the generating functions of random walk models can display a very broad range of analytic properties. In this context the kernel method becomes much richer and I will demonstrate a few of the ways in which it may be applied. Along the way I will give a few "warm-up" exercises and finish with a few open problems concerning more interacting boundary conditions.