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R for Data Science by Dan Toomey

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Summary

In this chapter, we looked into various methods of machine learning, including both supervised and unsupervised learning. With supervised learning, we have a target variable we are trying to estimate. With unsupervised, we only have a possible set of predictor variables and are looking for patterns.

In supervised learning, we looked into using a number of methods, including decision trees, regression, neural networks, support vector machines, and Bayesian learning. In unsupervised learning, we used cluster analysis, density estimation, hidden Markov models, and blind signal separation.

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