Check out the lecture notes for CS229, Stanford's class on Machine Learning. The class has no textbook, so the notes are fairly comprehensive. (Though the math notation can get a bit intense...) http://www.stanford.edu/class/cs229/materials.html
Also, the notes assume a reasonable knowledge of probability theory and linear algebra. If you're unsure, you might do well to review those topics before approaching machine learning (or at least keep good references handy). (Edit: the link above has Section Notes that review probability theory, linear algebra, and convex optimization. You might find those useful.)
And it's not an easy course. Don't be discouraged if the problem sets seem impossible. (They very nearly are.)