Machine Learning with XGBoost Using scikit-learn in Python
XGBoost is the most winning supervised machine learning approach in competitive modeling on structured datasets. This course will teach you the basics of XGBoost, including basic syntax, functions, and implementing the model in the real world.
What you'll learn
At the core of applied machine learning is supervised machine learning. In this course, Machine Learning with XGBoost Using scikit-learn in Python, you will learn how to build supervised learning models using one of the most accurate algorithms in existence. First, you will discover what XGBoost is and why it’s revolutionized competitive modeling. Next, you will explore the importance of data wrangling and see how clean data affects XGBoost’s performance. Finally, you will learn how to build, train, and score XGBoost models for real-world performance. When you are finished with this course, you will have a foundational knowledge of XGBoost that will help you as you move forward to becoming a machine learning engineer.
Table of contents
About the author
Mike has Bachelor of Science degrees in Business and Psychology. He started his career as a middle school psychologist prior to moving into the information technology space. His love of computers resulted in him spending many additional hours working on computers while studying for his master's degree in Statistics. His current areas of interests include Machine Learning, Data Engineering and SQL Server. When not working, Mike enjoys spending time with his family and traveling.