Teaches aspiring data scientists how to build predictive models and mine data for insights.

Introduces the fundamental techniques for data mining and machine learning. Discusses several basic learning algorithms, such as regression, kNN, decision trees, support vector machines, and neural networks. Applies techniques to common types of data. Implements data mining strategies following CRISP-DM. Evaluates accuracy and fit of machine learning algorithms using common validation strategies, including k-fold cross-validation. Coding is done in R. Presumes knowledge of data collection and shaping, plus statistics.

no outcomes defined