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Overview
Context
Do you know the basics of supervised learning and want to use models on real-world datasets? Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes.
XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries.
In this Extreme Gradient Boosting with XGBoost course offered by Data Camp, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models.
Programme Structure
Chapters include:
- Classification with XGBoost
- Fine-tuning your XGBoost model
- Regression with XGBoost
- Using XGBoost in pipelines
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- New York City, United States
Disciplines
Machine Learning View 213 other Short Courses in Machine Learning in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Prerequisites
- Supervised Learning with scikit-learn
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
-
International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
This course can be accessed for free with the Data Camp Premium or Teams subscriptions
Funding
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