The XGBoost Algorithm in Python Video

The XGBoost Algorithm in Python Video

Objectives:

1. Learn about XGBoost (eXtreme Gradient Boosting) along with its benefits.
2. Install the XGBoost library.
3. Become proficient in installing Anaconda and the XGBoost library on Windows, Linux, and Mac OS.
4. Implement the various XGBoost linear and tree learning models in Python.
5. Practice applying the XGBoost models using a medical data set.
6. Tune the various parameters that exist in Python. Parameter tuning is the art in machine learning.
7. Practice applying the three categories of parameter tuning: Tree Parameters, Boosting Parameters, and Other Parameters.
8. Become proficient in a number of parameters including max_depth, min_samples_leaf, and max_features.
9. Evaluate an XGBoost model.
10. Apply the two most important techniques of Train Test Split and Cross Validation. Perform predictions using the XGBoost algorithm.

Instructor: Dhiraj Kumar

Length: 1 hour

Access period: For one year starting from purchase date