notebook with support for code, text, mathematical expressions, inline plots and other rich media. • Support for interactive data visualization and use of GUI toolkits. • Flexible, embeddable interpreters to load into your own projects. • Easy to use, high performance tools for parallel computing. From http://ipython.org :
Still effective in cases where number of dimensions is greater than the number of samples. • Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. • Easy to use and train • Versatile: different kernel functions can be specified for the decision function.
awesome :D! • SVM is mature algorithm, straightforward to use and works well in most of the cases. • Being able to use Kernels allows SVM to learn complex decision boundaries. • Using LibSVM / LibLinear based libraries allow for reusing models across languages or at least prototyping in Python.