A lot of the Mackay book is on information/coding theory and while it will deepen an existing understanding of ML, its probably a roundabout introduction. ESL is a much better intro, especially for someone looking to apply ML.

That said, it is meant for people who are comfortable with math/stats; its much more statistics oriented than, say, Mitchell's book. But they do a good job of explaining things in non-math language. This book does a good job of exposing high level ML concepts (e.g. bias-variance tradeoff) but still teaches a lot of the standard methods & tools.

These are actually two of my favorite books on the subject and I can't recommend them both enough.

I'm mainly interested in Machine Learning algorithms. I've read the first 75 pages of this book, and skimmed most of Mackay, and I prefer this one. It goes into a lot more detail on the performance of different algorithms, how the handle sparse data, error rates and high dimensionality.

McKay's looks like a good book, but appears more applicable to pure information theory rather than ML specifically.

The McKay textbook is more about Bayesian inference and a few neural network chapters.

Hastie is more of a statistical approach with statistical rigor.