On the other hand going right into code examples is useful, including jumping right into getting real data downloaded and worked on.
I also do not have PRML, but Neural Networks for Pattern Recognition by Bishop is excellent (and includes many non-NN related items).
ESL is excellent and, to me, the best modern text in machine learning. It covers many of the topics in PCI both at a reasonable level and in much more depth and provides MANY references (100s?) to dig deeper.
"[t]he algorithm takes the difference between every pair of items and tries to make a chart in which the distances between the items match those differences"
This is referring to multidimensional scaling which is a large family of techniques, some of which attempt to achieve this goal (delta ~= d), but not necessarily. Granted, in this case the algorithm presented DOES attempt to do this. BUT, the algorithm presented is not "multidimensional scaling". It is an implementation (not even a good one) attempting to minimize one of many possible objective functions.
I noticed several more when I flipped through it initially, but this is the type of thing I'm referring to.
That said I have three complaints:
1 I think it tries to stay too high level on some areas where it is truly unavoidable and wouldn't scare anyone off to add a little depth.
2 I think it too often labels their implementation with a name that is associated with a family of techniques. This is simply going to embarrass/mislead the reader. In the interest of maintaining simplicity the book states several things that are flat out wrong.
3 No references. If someone wants to take the next step there is zero guidance. This is important because someone learning about a deep topic could read the book and a) think they understand it (not likely) and b) not have a clue where to look next.