In advance of each session, Tech Training will provide you with a Zoom link to your class, along with any required class materials.
A byte-sized session intended to explore different tools used in deploying machine learning models.
Prerequisite:
- Have a basic understanding of the Python language, pandas library and be familiar with how to use Jupyter Notebook.
- Have an understanding of how to build either Classification models or Regression models.
Audience:
- This session is designed for anyone who is familiar with machine learning model development and has an understanding of building Classification and/or Regression models.
Objectives:
During this course, you will have the opportunity to learn how to:
- Understand hyper parameters and how to tune them.
- Build a model and tune the parameters.
- Use K-Fold to create diverse test buckets while building the model.
- Perform grid search to find the best parameters.
Setup
- Because this is an abbreviated session, attendees MUST install Anaconda software https://www.anaconda.com/ prior to the class, and have a basic understanding of using Jupyter Notebook.
University IT Technology Training classes are only available to Stanford University staff, faculty, students and Stanford Hospitals & Clinics employees. A valid SUNet ID is needed in order to enroll in a class.