Prerequisite: Hands-On Introduction to Machine Learning for Data Science with Python
This course can also be delivered using R.
Any team can employ machine learning to analyze data and discover powerful insights into the business. This one-day intensive workshop is designed to springboard teams with foundational Python and machine learning skills into applying the xgboost machine learning algorithm to their business data.
The curriculum is designed specifically for any professional and does not require any previous background in advanced mathematics or statistics. Attendees build practical, actionable skills via hands-on labs using free, open-source software.
Attendees will receive a thorough introduction to the state-of-the-art xgboost algorithm, including how xgboost works, how to tune xgboost models for optimal predictive performance, and how to use SHAP to provide explanations for model predictions.
Your Team Will Learn
- Why decision tree ensembles are state-of-the-art
- What is a boosted decision tree ensemble
- Gradient boosting decision trees with xgboost
- Training xgboost models
- Xgboost hyperparameters
- Tuning xgboost models
- Evaluating the accuracy of xgboost models
- Using SHAP to explain xgboost model predictions
- Additional resources for honing skills
- Business and data analysts
- BI and analytics developers and managers
- Business users
- Data Scientists
- Anyone interested in using machine learning to analyze business data
Attendees will need a laptop computer with specific software installed before the session. In advance of the class, attendees will receive detailed software download and installation instructions.