I think it would be a really great first step to create a summary of the current state of machine learning in Julia here. We should show a list of relevant packages along with some high-level information about each one. It would also be great to have a few small examples/tutorials for "hello world" model fittings in some of the core packages.

Packages to put on the website (in no particular order):

• Mocha.jl
• MXNet.jl
• OnlineAI.jl
• KSVM.jl
• Clustering.jl
• DecisionTrees.jl
• Orchestra.jl
• LearnBase.jl
• MLBase.jl
• MLKernels.jl
• MachineLearning.jl
• https://github.com/dmlc/XGBoost.jl

What am I missing?

We can also link to https://github.com/svaksha/Julia.jl/blob/master/AI.md for anything we don't highlight.

Now would also be a good time for me to start work on MLPlots.jl (probably instead of StatPlots.jl, since no one showed any interest in that, and then I can combine things like ROC curves, etc). Then we could provide some common visualizations on the website as well.

I was thinking it would be cool to have a JuliaML blog which showcases some machine learning and visualization. Is anyone interested in participating? Any ideas for content?

Add a section so it is clear why this Org exists and what the differences are between these packages and others in the Julia ML Ecosystem (Flux and others)