Andrew Ng's course notes are 90% of the way to being a textbook, and are an incredible resource, especially if you're going to watch the lectures.

http://www.stanford.edu/class/cs229/materials.html

God, whatever you do please don't start by picking a few datasets at UCI ML site and using R packages to play with the data.

An ideal approach will be to:

- Pick any programming language and start off with plain regression. It may look simple but this will become fantastic base going forward

- Generate a synthetic data set and apply your freshly written regression on it

- Expand your toolkit to include test and training data set generation and calculation of ROC curves and confusion tables

- Add logistic regression, regularizers and other advanced regression models to the toolkit

- Use a real world dataset and develop multiple different models. And pick the best model (choosing the right model itself is a big task in itself)

- Then try coding Neural Networks, SVM, etc.

I would add:

Programming Collective Intelligence (O'reilly) http://oreilly.com/catalog/9780596529321

http://ianma.wordpress.com/2009/07/19/machine-learning-for-b...

Practical Artificial Intelligence Programming in Java http://www.markwatson.com/opencontent/

Nice to see a link to online courses. I've been studying Bishop's book in my spare time.

I'm curious what you guys use ML for