1. You can get a long way with high school calculus and probability theory.
2. Regarding books I second the late David McKay's "Information Theory, Inference and Learning Algorithms" and the second edition of "Elements of Statistical Learning" by Tibshirani et al. (there's also a more accessible version of a subset of the material targeting MBA students called James et al., An Introduction to Statistical Learning). Duda/Hart/Stork's Pattern Classification (2nd ed.) is also great.
The self-published volume by Abu-Mostafa/Magdon-Ismail/Lin, Learning from Data: A Short Course is impressive, short and useful for self-study.
3. Wikipedia is surprisingly good at providing help, and so is Stack Exchange, which has a statistics sub-forum, and of course there are many online MOOC courses on statistics/probability and more specialized ones on machine learning.
4. After that you will want to consult conference papers and online tutorials on particular models (k-means, Ward/HAC, HMM, SVM, perceptron, MLP, linear and logistic regression, kNN, multinomial naive Bayes, ...).
2. Regarding books I second the late David McKay's "Information Theory, Inference and Learning Algorithms" and the second edition of "Elements of Statistical Learning" by Tibshirani et al. (there's also a more accessible version of a subset of the material targeting MBA students called James et al., An Introduction to Statistical Learning). Duda/Hart/Stork's Pattern Classification (2nd ed.) is also great. The self-published volume by Abu-Mostafa/Magdon-Ismail/Lin, Learning from Data: A Short Course is impressive, short and useful for self-study.
3. Wikipedia is surprisingly good at providing help, and so is Stack Exchange, which has a statistics sub-forum, and of course there are many online MOOC courses on statistics/probability and more specialized ones on machine learning.
4. After that you will want to consult conference papers and online tutorials on particular models (k-means, Ward/HAC, HMM, SVM, perceptron, MLP, linear and logistic regression, kNN, multinomial naive Bayes, ...).