Summary

In this chapter, you have learned how to build a variety of ML models to solve regression and classification problems. You have implemented linear regression, Ridge, Lasso, logistic regression, decision trees, random forests, Naive Bayes, AdaBoost, and XGBoost. 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 value of learning these skills is that you can make meaningful and accurate predictions from big data using some of the best ML models in the world today.

In the next chapter, you will improve your ML skills by learning...