Applied Classification using XGBoost is a comprehensive course designed for data professionals who want to gain expertise in XGBoost, a powerful machine-learning tool for regression and classification. The course covers various aspects of XGBoost, including the algorithm, decision trees, boosting techniques, and exploratory data analysis.
The course starts with an introduction to the XGBoost library and provides a baseline model for understanding its capabilities. It then delves into the concept of decision trees and how they are utilized in XGBoost. Through Jupyter Notebook demos and real survey data, participants get hands-on experience implementing boosting techniques and conducting preliminary exploratory data analysis.
One of the unique features of this course is that it focuses on using third-party libraries to evaluate and explain data. Instead of using commonly used datasets like Iris and Titanic, real survey data is employed to provide a more realistic learning experience. By the end of the course, participants will be able to build a classifier model and work with raw data.
The course offers flexibility in terms of following the instructions on one's own computer or adapting the code to suit individual needs using the provided notebook. Whether it's training a classifier or gaining a deeper understanding of XGBoost, this course equips data professionals with the skills necessary to leverage XGBoost effectively.
In summary, Applied Classification using XGBoost is an intensive course that covers all aspects of XGBoost, from the basics of the algorithm to advanced techniques such as boosting. With real survey data and hands-on exercises, participants can gain practical knowledge in modeling classifiers and analyzing data using XGBoost.
