Description: A discussion on supervised learning--the class of machine learning where a model is trained to produce a specific result for a given input.

Description: Introduction to PMML and how to use of it in the R programming language. PMML is an interchange format for predictive models.

Description: Feature article discussing the use of Bayesian Networks as predictive modeling tools.

Description: Bayesian networks are one of many models that are useful for predictive modeling. This article introduces the concept of Bayesian network and shows how time-series data can be modeled for a Bayesian network.

Description: Deep learning is a method that allows neural networks to be constructed with more than just one or two hidden layers. These layers can then be trained independently and simultaneously of each other. This article provides an introduction to deep belief neural networks.

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

Description: Python & Scikit-Learn are a powerful combination for predictive modeling. Using Scikit-Learn you gain access to a variety of models, such as support vector machines, decision trees, gradient boosting and others. Scikit-Learn is very popular with the Kaggle community.

Description: This article introduces SymPy, a computer algebra system (CAS) for the Python programming language.

Description: An exploration of moving predictive modeling capabilities in R to real time operations.

Description: This article shows how to use a RBF Neural network to predict if a breast tumor is malignant. The network is trained using a genetic algorithm that uses C# multi-threading.