This repository includes tutorials and source code of the course of XGboost
.
This course is designed to provide an in-depth understanding of the XGBoost algorithm and its implementation in Python. It covers topics such as boosting algorithms, tree-based models, feature selection, hyperparameter tuning, and model interpretation.
Part | GitHub Doc | Project Files 📁 |
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Introduction | Link | - |
What is Gradient boosting | Link | - |
What is XGBoost | Link | - |
Alternatives to XGBoost | Link | - |
Installation | Link | - |
Anyone may contribute to our project. Submit a pull request or raise an issue.
Url | |
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My Resume | arashyeganeh.github.io |
My LinkedIn profile | @arash-yeganeh |
My GitHub | arashyeganeh |
My Kaggle | arashyeganeh |
👋 Hi, I’m Arash Yeganeh.
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