Tutorial on Tree Based Modeling

I found a useful tutorial on tree based learning

The tutorial includes

• What is a Decision Tree? How does it work?
• Regression Trees vs Classification Trees
• How does a tree decide where to split?
• What are the key parameters of model building and how can we avoid over-fitting in decision trees?
• Are tree based models better than linear models?
• Working with Decision Trees in R and Python
• What are the ensemble methods of trees based model?
• What is Bagging? How does it work?
• What is Random Forest ? How does it work?
• What is Boosting ? How does it work?
• Which is more powerful: GBM or Xgboost?
• Working with GBM in R and Python
• Working with Xgboost in R and Python
• Where to Practice ?