Summary

In this chapter, you have learned how to build a variety of machine learning models to solve regression and classification problems. You have implemented Linear Regression, Ridge, Lasso, Logistic Regression, Decision Trees, Random Forests, Naive Bayes, and AdaBoost. You have learned about the importance of using cross-validation to split up your training set and test set. You have learned about the dangers of overfitting and how to correct it with regularization. You have learned how to fine-tune hyperparameters using GridSearchCV and RandomizedSearchCV. You have learned how to interpret imbalanced datasets with a confusion matrix and a classification report. You have also learned how to distinguish between bagging and boosting, and precision and recall.

The truth is that you have only scratched the surface of machine learning. In addition to classification and regression, there are many other popular classes of machine learning algorithms, such as recommenders, which are...