Machine Learning with Python: Zero to GBMs Machine Learning with Python: Zero to GBMs A beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python and its ecosystem of ML libraries: scikit-learn, XGBoost, and LightGBM. Earn a verified certificate of accomplishment by completing practical assignments and building a real-world course project. Watch hands-on coding-focused video tutorials Practice coding with cloud Jupyter notebooks Build an end-to-end real-world course project Earn a verified certificate of accomplishment Interact with a global community of learners You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world dataset. Prerequisite: Data Analysis with Python: Zero to Pandas . Lesson 1 - Linear Regression with Scikit Learn Preparing data for machine learning Linear regression with multiple features Generating predictions and evaluating models Lesson ...