Do you know the basics of supervised learning and want to learn 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 that has taken data science by storm, with models using XGBoost regularly winning many online data science competitions and 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 as well as regression problems.
Extreme Gradient Boosting with XGBoost features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will make you an expert in gradient boosting!
What you'll learn
1. Classification with XGBoost
The first 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, namely, predicting whether a customer will stop being a customer at some point in the future.
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. Along the way, 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
Here, you'll 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, as well as be introduced to some more advanced preprocessing techniques, all the while applying everything you've learned in the first three chapters.
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