https://books.google.com/books?id=5fvmDAAAQBAJ&printsec=fron...
I'm (hopefully) just starting a new position where some of the team is using machine learning techniques in an applied sense (i.e. it's not a CS position, they're not researching new methods, just applying existing ones). I'd like to get a good overview of common mistakes, pitfalls, etc. to look out for from a newcomer's perspective. I have used neural networks naively and briefly in a project about 6 years ago, but I know a lot(!) has changed since then.
http://www.abebooks.com/9780070428072/Machine-Learning-Tom-M...
https://www.amazon.com/Artificial-Intelligence-Modern-Approa...
I would be curious if anyone has gone through this book and what their thoughts were on it.
Plenty of top universities put their ML material online, so I would pick your favorite and check it out. My school teaches roughly based on the Bishop book (I think he keeps a free pdf online). It's dense reading but has information on a huge array of topics. Someone else may be able to suggest other books that are a little more focused.
Really though, I would just pick a school, look at their course website, get their textbook, and work through the posted material at whatever pace you feel provides the value you are looking for.
There are not many blogs out there about the topic as far as I know, but I can recommend Chris Olah's blog.
Once I left the university and started doing market and customer research in business that kind of raw prediction/black box modeling didn't fly very well. Companies preferred simpler models with explainable connections between independent variables that they could leverage and dependent variables (business outcomes).