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Congratulations!

1. Congratulations!

Congratulations on completing this course!

2. How far you have come

Take a moment to take a look at how far you have come! In chapter 1, you started off by understanding and applying the CART algorithm to train decision trees or CARTs for problems involving classification and regression. In chapter 2, you understood what the generalization error of a supervised learning model is. In addition, you also learned how underfitting and overfitting can be diagnosed with cross-validation. Furthermore, you learned how model ensembling can produce results that are more robust than individual decision trees. In chapter 3, you applied randomization through bootstrapping and constructed a diverse set of trees in an ensemble through bagging. You also explored how random forests introduces further randomization by sampling features at the level of each node in each tree forming the ensemble. Chapter 4 introduced you to boosting, an ensemble method in which predictors are trained sequentially and where each predictor tries to correct the errors made by its predecessor. Specifically, you saw how AdaBoost involved tweaking the weights of the training samples while gradient boosting involved fitting each tree using the residuals of its predecessor as labels. You also learned how subsampling instances and features can lead to a better performance through Stochastic Gradient Boosting. Finally, in chapter 5, you explored hyperparameter tuning through Grid Search cross-validation and you learned how important it is to get the most out of your models.

3. Thank you!

I hope you enjoyed taking this course as much as I enjoyed developing it. Finally, I encourage you to apply the skills you learned by practicing on real-world datasets.

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