Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. This book develops supervised learning techniques for clustering (hierarchical clustering, non hierarchical clustering, Gaussian Mixture Models, Hidden Markov Models, Nearest Neighbors. KNN Classifiers, cluster visualization, Clusters with Self Organizing Map, Competitive Neural Networks, Competitive Layers, Autoencoders and clustering whit Neural Networks).
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