DATA SCIENCE THROUGH PYTHON. SUPERVISED LEARNING TECHNIQUES: kNN, SVM, NAIVE BAYES, BAGGING, BOOSTING, STACKING, AND NEURAL NETWORKS

Data Science 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 of the supervised learning techniques are developed throughout this book from a methodological point of view and from a practical point of view with applications through Python software. The following techniques are explored 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.