Currently working in Octave, but due to the poor documentation progress is very slow.
What language is easy to learn and use, and well documented to solve machine learning problems? I am looking to prototype on a small dataset (thousands of examples), so speed is not important.
EDIT: I am developing a recommendation engine. So, I am interested in using Regularized Linear Regression, Neural Nets, SVN or Collaborative Filtering.
Best Answer
If you want to use something out of box, Weka could be a great starting point. There is no need to program anything. You import your data, visualize it and play around with different models.
Next up in chain would be R. There is some learning curve associated - especially with munging your data to fit into R data structures but once you get over that, you have tons of libraries which offer all the machine learning capabilities without much effort.
Next up would be hand programming the machine learning algorithms. Since you are already using Octave and looking for alternatives, maybe what you want is not to hand code algorithms in some other system but to just use the libraries written by other people.
If you go down the R path, you might find book by Luis Torgo (Data Mining with R: Learning with Case Studies) very useful (disclosure: no affiliation). It describes in depth case studies which you can adapt to your problem.