Course curriculum

  • 1

    Introduction

    • Course Introduction

    • What is XGBoost?

    • Demo: XGBoost Pima Indian Diabetes

    • Summary
  • 2

    Gradient Boosting

    • Section Introduction

    • Gradient Boosting and the Decision Tree

    • Recursive Binary Splitting

    • Demo: Label Encoding with XGBoost

    • Ensembles

    • Bagging and Boosting

    • Demo: Kfold Cross Validation with XGBoost

    • Summary
  • 3

    Going Deeper with XGBoost

    • Section Introduction

    • Demo: Handling Missing Data

    • Demo: Serialize a Model with Pickle

    • Demo: Importance Scores using XGBoost

    • Demo: Caution using Importance Scores in XGBoost

    • Demo: Monitor Model Performance

    • Demo: Model Evaluation using Learning Curves

    • Demo: Early Stopping in XGBoost

    • Demo: Regression Model in XGBoost

    • Demo: Parallelism in XGBoost

    • Demo: Hyperparameter Default Recommendations

    • Demo: Tuning the Number of Decision Trees

    • Demo: Tuning Row Subsampling

    • Demo: Tuning the Learning Rate

    • Demo: Kaggle Top Titanic Model

    • Summary