Most supervised learning techniques are developed throughout this book from both a methodological and a practical perspective, with applications using Python software. The following techniques are explored in depth: Nearest Neighbor (kNN), Support Vector Machine (SVM), Naive Bayes, Assembly Methods, Bagging, Boosting, Voting, Stacking, Blending, Random Forest, Neural Networks, Multilayer Perceptron, Radial Basis Networks, Hopfield Networks, LSTM Networks, Recurrent Neural Networks (RNNs), Group Resource Units (GRUs), and Neural Networks for Time Series Prediction.