A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Tom Mitchell's quote is well-known and time-tested in the world of machine learning, having first appeared in his 1997 book. The sentence has been influential on me, personally, as I have referred to it numerous times over the years and referenced it in my Master's thesis. The quote also features prominently in Chapter 5 of the much more recent and authoritative "Deep Learning" by Goodfellow, Bengio & Courville, serving as the jumping off point for the book's explanation of learning algorithms. While inherently abstract, the variables E, T, and P can be mapped to machine learning algorithms and their learning processes in order to help solidify one's understanding of learning algorithms abstractly and even more concretely. Let's have a look at how we can get quite a bit of mileage out of such a succinct few words.