Is the a "machine learning for dummies" resource you would recommend?
A good place to start would be the scikit learn tutorials on youtube: https://www.youtube.com/watch?v=r4bRUvvlaBw I remember going through them and thinking---wow, this covers everything from my ML class in just a few iPython notebooks...
I recommend you start with the basics (datasets, features, training/test/validation data splits) and don't worry too much about the actual choice of model---there will always be shiny new models with better performance but sometimes using the "old stuff" is good enough.
Once you get past the basics want to learn the theory, you can take an online course or find a good book, e.g. https://www.cs.ubc.ca/~murphyk/MLbook/ (advanced, but very comprehensive).
Or, conversely, it may be that all models are just as bad. This seems to be the case in my domain (formal proofs), where the bottleneck seems to be data representation; it doesn't matter which learning algorithm you use, when your feature selection has stripped out all of the learnable information ;)
Two terms that come up when you're testing the model: recall and precision. I found these terms a bit unintuitive. Basically 'recall' is how many of the real matches did you manage to capture (accurately classify/predict), and 'precision' is how wasteful your model was (how many false alarms). Depending on what you're doing one of those things may be much more important than the other.
I can actually imagine a world where running a machine learning model is a kind of mundane office task that most people can do, like creating a pivot table.
It's as good a place to start as any, and the benefit of a scheduled class is that you'll have a community doing the same work at the same time to help you out.
Torch (Lua) is similar, but is not symbolic, and when you want to calculate the gradient, it will do the backpropagation through the same graph, just backwards. It's like Theano only a mathematical framework, which is very useful for all kinds of NNs but also any other mathematical models.
There are many libs based on Theano, e.g. like Groundhog, Keras, Lasagne, etc. There are also Torch based NN utils. You usually code your model directly in Python / Lua. Thus that is very flexible.
Caffe is a C++ framework with CUDA support. You describe your models by some declarative config. It's thus much less flexible.
PyBrain (Python) is similar to Groundhog etc, but not based on Theano but on pure Python / Numpy code.
Brainstorm (Python) is similar to PyBrain, and also not based on Theano but is has several own custom backends, including CUDA.