Machine Learning with XGBoost

Join us for this hands-on training where you will learn how to use XGBoost to create powerful prediction models using gradient boosting. Using Jupyter Notebooks you'll learn how to efficiently create, evaluate, and tune XGBoost models. This session runs for three hours, allowing you time to really immerse yourself in the subject.

What will I learn?

• How to decide whether to use gradient boosting or not

• How to instantiate and customize XGBoost models

• How to use XGBoost's DMatrix to optimize computing performance

• How to evaluate models in XGBoost using the right metrics

• How to tune parameters in XGBoost to achieve the best results

• How to visualize trees in XGBoost to analyze feature importance

What should I prepare?

Please note, a Gmail account is required in order to use Colaboratory, a free Jupyter notebook environment. You can join the webinar from your web browser following the instructions you receive in your registration email. All required data/resources will be provided in the training.

Who should attend?

This course is open to all DataCamp Premium learners, looking to boost their machine learning skillset.

Machine Learning with Tree-Based Models in Python Extreme Gradient Boosting with XGBoost

Resources

• Slides 

• Student Notebook

• Solution Notebook

Lis Sulmont

Curriculum Manager at DataCamp