Ultimately, ML is a mathematical discipline. You can ask for a gentle approach that gets you to the foot of the mountain, but "if you want to learn about nature, to appreciate nature, it is necessary to understand the language that she speaks in." If you want to be more than an amateur, there's not much substitute for getting comfortable with math at the level of, say, Kevin Murphy's book.
The good news is that the required math is fairly elementary - calculus, linear algebra, probability and statistics, all freshmen or maybe sophomore-level topics - so it shouldn't be beyond reach of a motivated developer able to set aside some time to learn. MOOCs and organizing study groups with friends/co-workers can help a lot here as well.
Machine Learning for Hackers http://www.amazon.co.uk/Machine-Learning-Hackers-Drew-Conway...
Design for Hackers http://www.amazon.co.uk/Design-Hackers-Reverse-Engineering-B...
Bayesian Methods for Hackers http://www.amazon.co.uk/Bayesian-Methods-Hackers-Probabilist...
EDIT: I'm not the author, but you can find Bayesian Methods for Hackers (free, released by the author) at the link below. I think it's a great resource for anyone wanting to explore Bayesian methods using Python.
https://github.com/CamDavidsonPilon/Probabilistic-Programmin...
http://shop.oreilly.com/product/0636920018483.do http://www.amazon.com/Machine-Learning-for-Hackers-ebook/dp/...
It's definitely a practical approach. There's a lot of explanation but, despite coming from two PhD candidates, it certainly did not read like an academic paper.
It's also case study based, not necessarily algorithm based.
As a bonus, I used that book to teach myself R. I don't think it's meant to be an intro to R tutorial, but it worked for me.
All three are positives in my book.
http://shop.oreilly.com/product/0636920018483.do http://www.amazon.com/Machine-Learning-for-Hackers-ebook/dp/...
It's definitely a practical approach. There's a lot of explanation but, despite coming from two PhD candidates, it certainly did not read like an academic paper.
It's also case study based, not necessarily algorithm based.
As a bonus, I used that book to teach myself R. I don't think it's meant to be an intro to R tutorial, but it worked for me.
All three are positives in my book.
http://machinelearning.reddit.com
http://shop.oreilly.com/product/9780596529321.do
http://www.amazon.com/Algorithms-Intelligent-Web-Haralambos-...
http://www.amazon.com/Mahout-Action-Sean-Owen/dp/1935182684/...
http://www.amazon.com/Collective-Intelligence-Action-Satnam-...
http://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/d...
http://www.amazon.com/Machine-Learning-Action-Peter-Harringt...
Otherwise, this is a bit confusing.