XGBoost Deep Dive w/ Python & Pandas Hands-on Data Science

The XGBoost Deep Dive course is a comprehensive program that teaches students the top skills they need to become a Python machine learning engineer or data scientist. The course focuses on using the Python version of XGBoost, the best and most popular algorithm for tabular data, and teaches students how to use it effectively for a variety of machine learning tasks. Throughout the course, students will learn how to leverage Pandas for feature engineering and data visualization, and will understand how to define a machine learning project, going from raw data to a trained model.

They will also learn about gradient boosting decision trees and will work with realistic datasets and hands-on projects to apply their knowledge in a practical setting. In addition, students will learn how to apply XGBoost to Natural Language Processing (NLP) problems using deep learning (Sentence Transformers) and TF-IDF features. The course includes five hands-on projects with Python:A supervised regression problem where students predict Airbnb listing prices.

A binary classification problem where students work with actual logs of website visits to predict online conversions. A multi-class classification problem where we would predict the credit rating of customers in 3 categoriesA multi-class text classification problem where students work with large datasets and more than 200 classes. A time series forecasting problem where students use XGBoost to make predictions.

By the end of the course, students will have a strong understanding of how to use XGBoost, Pandas and Python and will be able to apply these skills to their own machine learning and data science projects.