What you’ll learn
- Learn the top skill to become a Machine Learning Engineer or Data Scientist
- Learn XGBoost, the best and most popular algorithm for tabular data
- Leverage Pandas for Feature Engineering and data Visualization
- Understand how to define a machine learning project, going from raw data to a trained model
- Learn Gradient Boosting Decision Trees working with realistic datasets and Hands on projects
- Learn to apply XGBoost to NLP problems using Deep Learning and TF-IDF features
- Project 1: Supervised Regression problem where we predict AirBnB listings prices
- Project 2: Binary Classification problem where we work with actual logs of a website visits to predict online conversions
- Project 3: Multi Class text Classification. We work with large datasets and more than 200 classes
- Project 4: Time series Forecasting with XGBoost
The XGBoost Deep Dive course is a comprehensive program that teaches students the top skills they need to become a machine learning engineer or data scientist. The course focuses on 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 and TF-IDF features.
The course includes five projects:
- 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 categories
- A 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 and will be able to apply these skills to their own machine learning and data science projects.