Overview

By the end of this chapter, you will be able to apply machine learning (ML) algorithms to solve different problems; compare, contrast, and apply different types of ML algorithms, including linear regression, logistic regression, decision trees, random forests, Naive Bayes, Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost); analyze overfitting and implement regularization; work with GridSearchCV and RandomizedSearchCV to adjust hyperparameters; evaluate algorithms using a confusion matrix and cross-validation, and solve real-world problems using the ML algorithms outlined here.