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Extreme Gradient Boosting with XGBoost

Intermediate

4.2+

Updated 12/2024

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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PythonMachine Learning4 hours16 videos49 exercises3,750 XP53,008Statement of Accomplishment

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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

Supervised Learning with scikit-learn

1

Classification with XGBoost

Welcome to the course!

Which of these is a classification problem?

Which of these is a binary classification problem?

Introducing XGBoost

XGBoost: Fit/Predict

What is a decision tree?

Decision trees

What is Boosting?

Measuring accuracy

Measuring AUC

When should I use XGBoost?

Using XGBoost

2

Regression with XGBoost

Regression review

Which of these is a regression problem?

Objective (loss) functions and base learners

Decision trees as base learners

Linear base learners

Evaluating model quality

Regularization and base learners in XGBoost

Using regularization in XGBoost

Visualizing individual XGBoost trees

Visualizing feature importances: What features are most important in my dataset

3

Fine-tuning your XGBoost model

Why tune your model?

When is tuning your model a bad idea?

Tuning the number of boosting rounds

Automated boosting round selection using early_stopping

Overview of XGBoost's hyperparameters

Tuning max_depth

Tuning colsample_bytree

Review of grid search and random search

Grid search with XGBoost

Random search with XGBoost

Limits of grid search and random search

When should you use grid search and random search?

4

Using XGBoost in pipelines

Review of pipelines using sklearn

Exploratory data analysis

Encoding categorical columns I: LabelEncoder

Encoding categorical columns II: OneHotEncoder

Encoding categorical columns III: DictVectorizer

Preprocessing within a pipeline

Incorporating XGBoost into pipelines

Cross-validating your XGBoost model

Kidney disease case study I: Categorical Imputer

Kidney disease case study II: Feature Union

Kidney disease case study III: Full pipeline

Tuning XGBoost hyperparameters

Bringing it all together

Final Thoughts

Extreme Gradient Boosting with XGBoost

Course
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*4.2

from 33 reviews

52%

27%

12%

9%

0%

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Most recent
Top reviews

Orlando R.

3 months

Excellent instructor. He explains a lot of difficult concepts with ease. The examples were great so as the exercises. Highly recommended.

Thomas M.

9 months

Exzellent!

Louis B.

11 months

Good coverage of an important topic.

Scott C.

11 months

I originally was introduced to XGBoost by the Tree based models course with R, and wanted learn additional material about it at a lower levl, and thought the python course would help. I was very satisfied and found the instructor easy to follow.

KB M.

about 1 year

This course is superb. I enjoyed every single section of it. The instructor exudes passion and demonstrates a clear understanding of XGB's inner workings.

"Excellent instructor. He explains a lot of difficult concepts with ease. The examples were great so as the exercises. Highly recommended."

Orlando R.

"Exzellent!"

Thomas M.

"Good coverage of an important topic."

Louis B.

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