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.
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.
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.
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).