XGBoost Deep Dive w/ Python & Pandas | Hands-on Data Science
Master XGBoost, Pandas, and Python in this comprehensive course for aspiring Machine Learning Engineers and Data Scientists. Hands-on projects included.
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 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 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, Pandas and Python and will be able to apply these skills to their own machine learning and data science projects.
Who this course is for:
- Python Developers with some experience working with data
- Data Analysts that want to transition to Data Science or a Machine Learning Engineer Role
- Developers with some python experience that want to learn some machine learning with real world projects
- Data Scientists that want to learn more about XGBoost from a practical, applied standpoint
- University students that want to get some Hands-On experience with XGBoost
