Decision tree models in Python | Udemy Free Download

Decision tree models in Python | Udemy
English | Size: 464.61 MB
Genre: eLearning

What you’ll learn
Decision trees and how they work for regression and classification
Random forest
Extremely randomized trees
Gradient Boosting Decision Trees
XGBoost

In this practical course, we are going to focus on the decision tree machine learning models using Python programming language.

Decision trees are a particular and very effective type of model of the machine learning landscape. They try to predict the output variable according to particular binary decision rules to apply to the features. The best split that satisfies the rule is found during the training phase.

Decision trees express their best power when used in an ensemble. This way, we get models like random forest and extremely randomized trees (if we use bagging) and gradient boosting decision trees (if we use boosting).

With this course, you are going to learn:

Theory of the decision trees, with several splitting criteria for regression and classification

Hyperparameters of the decision trees

Random forest and its hyperparameters

Extremely randomized tree and its hyperparameters

Gradient Boosting Decision Tree and its hyperparameters

XGBoost and its hyperparameters

All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.

This course is part of my Supervised Machine Learning in Python online course, so you’ll find some lessons that are already included in the larger course.

Who this course is for:
Python developers
Data Scientists
Computer engineers
Researchers
Students