In this chapter, we have discussed supervised learning, such as classification, the k-nearest neighbors algorithm, Bayes classifiers, reinforcement learning, and the RTextTools and sklearn modules in R. In addition, we discussed implementations of supervised learning via R, Python, Julia, and Octave.
For the next chapter, we will discuss predictive data analytics, modeling and validation, some useful datasets, time-series analytics, how to predict the future, seasonality, and how to visualize our data. For Python packages, we will mention predictives-models-building, model-catwalk, and easyML. For R packages, we will discuss datarobot, LiblineaR, eclust, and AppliedPredictiveModeling. For Julia packages, we will explain EmpiricalRisks ...
