Machine Learning with the Wolfram Language

This chapter introduces the gradient descent algorithm as an optimization method for linear regression; the corresponding computations are shown, as well as the concept of the learning curve of the model. Later, you see how to use the specialized functions of the Wolfram Language for machine learning, such as Predict, Classify, and ClusterClassify, in the case of linear regression, logistic regression, and cluster search. The different objects and results generated by these functions and the metrics to measure the model are shown for these functions. In each case, the parts of the model that are fundamental for the correct construction using the Wolfram Language are explained. This part of the book uses examples of known datasets such as the Fisher’s Irises, Boston Homes, and Titanic datasets.