Machine Learning with Tree-Based Models in Python

Decision trees can be used to solve problems that relate to regression and classification. Tree models have a high degree flexibility but come at a price: They can capture complex nonlinear relations while being more likely to recall the noise in a dataset. Ensemble methods combine predictions from different trees to increase flexibility and decrease their tendency to forget about noise. These methods are versatile and have been awarded numerous machine learning awards.

This course will show you how to use Python and scikit-learn to create decision trees. You'll be working with real data and learn about the advantages and disadvantages of trees, as well as how ensembling may help to mitigate them. To make your models more efficient, you'll also learn how important hyperparameters can be tuned.