Applied Data Mining/Machine Learning and Predictive Analytics

Catalog description

Addresses the complete process of building analytical tools suitable for learning from data, including automatic online data collection, feature extraction, supervised and unsupervised statistical machine learning methods, evaluation, and report writing.  Automatic retrieval of various format online data, including JSON, REST, and Streaming API, http(s), html, xml, and databases.  Statistical text processing/mining, state of the art supervised and unsupervised data mining methods, case studies and applications to business, government,social and news media data.  Methods include regularized linear and logistic regression, classification trees, nearest neighbor methods, support vector machines, naive Bayes, random forests, boosting/bagging/AdaBoost, clustering, latent Dirichlet allocation, network analysis, and topic modeling models.