Learn XGBOOST with Real Code Examples

Learn Python and scikit-learn basics

Understand decision trees and gradient boosting

Practice XGBoost on classification and regression

Explore hyperparameter tuning and early stopping

Integrate into production ML pipelines

Week 1: train basic classifier/regressor

Week 2: hyperparameter tuning and cross-validation

Week 3: ranking tasks and custom objective functions

Week 4: GPU training and distributed learning

Week 5: deployment and monitoring in pipelines

Explain gradient boosting and XGBoost's improvements over classic GBM.

How does XGBoost handle missing values?

Difference between exact and approximate tree methods?

How to prevent overfitting in XGBoost?

Compare XGBoost with LightGBM and CatBoost

xgb.XGBClassifier() = classification model

xgb.XGBRegressor() = regression model

xgb.DMatrix() = optimized dataset format

xgb.train() = train booster with parameters

predict() = generate predictions

Hands-On Gradient Boosting with XGBoost

Mastering Machine Learning with XGBoost

Applied Boosting Techniques in Python

Tabular ML with XGBoost and LightGBM

Practical Machine Learning with XGBoost

XGBoost official tutorials

Kaggle example notebooks

Medium blogs on XGBoost tips

YouTube tutorials on gradient boosting

Hands-on tabular ML courses with XGBoost

https://xgboost.readthedocs.io/

https://github.com/dmlc/xgboost

XGBoost GitHub

StackOverflow XGBoost tag

Kaggle forums

Reddit ML and Kaggle communities

Blogs and online tutorials

XGBoost GitHub repository

StackOverflow XGBoost tag

Kaggle forums and competitions

Medium and blog tutorials

Reddit ML communities