After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Do you know the basics of supervised learning and want to use state-of-the-art 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 course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models. You'll work with real-world datasets to solve classification and regression problems.
Prerequisites
Curriculum
Course outline
1
Classification with XGBoost
This chapter will introduce you to the fundamental idea behind XGBoost—boosted learners. Once you understand how XGBoost works, you'll apply it to solve a common classification problem found in industry: predicting whether a customer will stop being a customer at some point in the future.
- Welcome to the course!50 XP
- Which of these is a classification problem?50 XP
- Which of these is a binary classification problem?50 XP
- Introducing XGBoost50 XP
- XGBoost: Fit/Predict100 XP
- What is a decision tree?50 XP
- Decision trees100 XP
- What is Boosting?50 XP
- Measuring accuracy100 XP
- Measuring AUC100 XP
- When should I use XGBoost?50 XP
- Using XGBoost50 XP
2
Regression with XGBoost
After a brief review of supervised regression, you'll apply XGBoost to the regression task of predicting house prices in Ames, Iowa. You'll learn about the two kinds of base learners that XGboost can use as its weak learners, and review how to evaluate the quality of your regression models.
3
Fine-tuning your XGBoost model
This chapter will teach you how to make your XGBoost models as performant as possible. You'll learn about the variety of parameters that can be adjusted to alter the behavior of XGBoost and how to tune them efficiently so that you can supercharge the performance of your models.
4
Using XGBoost in pipelines
Take your XGBoost skills to the next level by incorporating your models into two end-to-end machine learning pipelines. You'll learn how to tune the most important XGBoost hyperparameters efficiently within a pipeline, and get an introduction to some more advanced preprocessing techniques.
Extreme Gradient Boosting with XGBoost
Course
Complete
