MACHINE LEARNING THROUGH PYTHON. SUPERVISED LEARNING: NEAREST NEIGHBOR, NAIVE BAYES, MODEL ENSEMBLE, AND NEURAL NETWORKS

Machine learning algorithms use computational methods to extract information directly from data. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most supervised learning techniques are developed throughout this book from a methodological and practical point of view with applications through the Python software. The following techniques are covered in depth: Nearest Neighbor (kNN), Support Vector Machine (SVM), Naive Bayes, Ensemble Methods, Bagging, Boosting, Voting, Stacking, Blending, Random Forest, Neural Networks, Multilayer Perceptron, Radial Basis Networks, Hopfield Networks, LSTM Networks, Recurrent Networks (RNN), GRU Networks, and Neural Networks for Time Series Prediction.