Data science life cycle, exploratory data analysis, data visualization, data preprocessing, dimensionality reduction and feature selection, linear and polynomial regression, over fitting and regularization, logistic regression, neural networks, K-nearest neighbors, linear discernment analysis, support vector machines, ensembles methods, Bayesian networks, hidden Markov model, model selection and assessments, cluster analysis, K-means, hierarchal clustering, EM and mixture models – EM-GMM, cluster validation methods, reinforcement learning.
