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8 Best Free Resources To Learn XGBoost

8 Best Free Resources To Learn XGBoost

  • Data scientists use XGBoost to solve classification, regression, user-defined prediction problems etc.

XGBoost or eXtreme Gradient Boosting is a popular scalable machine learning package for tree boosting. Data scientists use it extensively to solve classification, regression, user-defined prediction problems etc. The speed, high-performance, ability to solve real-world scale problems using a minimal amount of resources etc., make XGBoost highly popular among machine learning researchers.

Here is a list of the top eight free resources to learn XGBoost.

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(The list is in no particular order)

1| XGBoost Tutorials

About: XGBoost Tutorials covers various topics to help you understand XGBoost from scratch. This official document will explain boosted trees in a self-contained and principled way using the elements of supervised learning. The tutorials deliberate on: an introduction to Boosted trees; distributed XGBoost with AWS YARN; distributed XGBoost on Kubernetes; Random Forests in XGBoost; using XGBoost External Memory Version and more.

Know more here.

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2| How XGBoost Works

About: This tutorial is put together by AWS, where you can learn all about the XGBoost method and how it works. You can also grasp related topics, such as XGBoost algorithms, how gradient tree boosting works, hyperparameters, supervised learning etc. 

Know more here.

3| Find Actionable Insights Using Machine Learning And XGBoost

About: Udemy’s ‘Find Actionable Insights using Machine Learning and XGBoost’ is all about practical experience. You will learn how to explore student data, model student behaviour using XGBoost, predict struggling/at-risk students, identify what makes a struggling student different from successful students, build a report of actionable insights, etc.   

Know more here.

4| Using XGBoost in Python

About: This tutorial will guide you on building machine learning models using XGBoost in Python. More specifically, you will learn: what Boosting is and how XGBoost operates; how to apply XGBoost on a dataset and validate the results; various hyper-parameters that can be tuned in XGBoost to improve model’s performance; how to visualise the Boosted Trees and Feature Importance, etc.

Know more here.

5| A Gentle Introduction to XGBoost for Applied Machine Learning

About: LAs the name suggests, ‘A Gentle Introduction to XGBoost for Applied Machine Learning’ will give you a gist of what XGBoost is, where it came from and how you can learn more. You will understand the goal of this project, why this package must be a part of your machine learning toolkit and where you can find more learning materials to up your game.

Know more here.

6| XGBoost: A Scalable Tree Boosting System

About: In this research paper, creator XGBoost, Tianqi Chen, explains why he created this package and how it can provide maximum performance in machine learning models. 

Know more here.

7| Understanding XGBoost Algorithm In Detail

About: The tutorial gives a brief introduction to what XGBoost is and how the package works internally to make decision trees and deduce predictions. The tutorial touches on various tree-based techniques, features of XGBoost, and an example of how XGBoost helps predict a child’s IQ based on age.

Know more here.

8| XGBoost

About: This tutorial, compiled by Kaggle Grandmaster and founder of decision.ai, DanB, will teach you how to build and optimise models with the powerful XGBoost library. The tutorial will help you understand the full modelling workflow with XGBoost and how to fine-tune XGBoost models for optimal performance.

Know more here.

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