Live training

Machine Learning with XGboost

Join us for this live, 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 will run for three hours, allowing you time to really immerse yourself in the subject, and includes short breaks and opportunities to ask the expert questions throughout the training

Tuesday 23 June, 1 PM EDT, 6 PM BST
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Python

What will I learn?

You will learn how to:

  • 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. We recommend that you have taken the following course before attending:

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

Presenter Bio

Lis Sulmont Headshot

Lis Sulmont

Workspace Architect

Lis is the Workspace Architect at DataCamp. She holds a Master's degree in Computer Science from McGill University with a focus in computer science education research and applied machine learning. Lis is passionate about teaching all things related to data and improving the accessibility of these topics.
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