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.
The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it
— that’s going to be a hugely important skill in the next decades.