I would have to say that the most complete software environment for Machine Learning and nonparametric modeling is R. This is a big field in statistics, spanning K-NN, Kernel smoothing, General Additive Models, weak learners, support vectors, neural nets, semi-parametric spline regression, imputation, etc... I would highly recommend reading: Hastie, T., R. Tibshirani, J. Friedman (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Series in Statistics.
Besides R, commercial software by Salford Systems has Random Forests, Multivariate Adaptive Regression Splines, CART and Gradient Boosting (TreeNet) available in a GUI environment. RuleQuest is still selling See5/C5 which is an updated version of the C4/ID3 CART algorithm. The University of Waikato's Weka 3 is an open source GUI/Commandline Java effort with a large number of models available.