ABSTRACT
Machine learning continues to grow in importance for many organizations across nearly all domains. The learning algorithm in a predictive model attempts to discover and model the relationships among the target variable and the other features. Unsupervised learning is often performed as part of an exploratory data analysis. The outputs of unsupervised learning models can be used as inputs to downstream supervised learning models. A predictive model is used for tasks that involve the prediction of a given output using other variables in the data set. Some dimension reduction techniques can be used to reduce the feature set to a potentially smaller set of uncorrelated variables. Such a reduced feature set is often used as input to downstream supervised learning models. The chapter also presents an overview of the key concepts discussed in this book.
