Imbalanced Classification Master Class in Python NED the XGBoost algorithm for imbalanced classification, it is important to test the default XGBoost model and establish a baseline in performance. Although the XGBoost library has its own Python API by Mike West What you'll learn You'll be able to add your rankings on Kaggle to your resume You'll be able to take what you've learned in the course and apply it to the real world You'll understand the machine learning workflow You'll learn why a class of models known as gradient boosters have taken over competitive modeling You'll learn how to tune an XGBoost model The majority of the course is programmtic with real-world code samples Description "An in depth course on XGBoost with code, examples and caveats. I would recommend to someone with a bit of ML experience, not for beginners (as he says in the first lecture)." To elaborate on the who-this-is-for section, if you know machine learning but not XGBoost specifically, this is for you." Louis B "Great code samples to get started on my own problems. Thanks!" Stephen E. Welcome to XGBoost Master Class in Python.