I'm new to machine learning and have only a vague idea of what it encompasses. In looking through the table of contents of "Machine Learning in Action", it seems like it contains an assortment of seemingly unrelated techniques for solving certain types of problems. What I'm wondering is if there is an overarching theme or philosophy which unites these different techniques together? Thanks!

Yes, in their most raw form: empirical data (input), mathematics (largely statistics giving the algorithm/process used in "learning"), inference (output/estimate).

Essentially the philosophy is that the underlying process that determines an outcome that we wish to derive is unknown or very complex. By using empirical data and a certain amount of mathematical/statistical sophistication we can arrive at an estimate that is more accurate on average than a hard-coded (i.e. non-learning based) algorithm. In many cases, we do not know enough about the process to even write an algorithm to describe it adequately; instead, we "let the data speak for itself" in determining a mathematical relationship.

With a little knowledge, a cast iron skillet is non-stick and lasts a lifetime.