Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
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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.
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, namely, predicting whether a customer will stop being a customer at some point in the future.
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!
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.
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. Enjoy!
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, namely, predicting whether a customer will stop being a customer at some point in the future.
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.
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!
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. Enjoy!
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