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Machine Learning/AI Series: Optimizing Machine Learning Models

Class Code


Class Description

Effective immediately in response to COVID-19, all Technology Training classes will be delivered online until further notice.

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.

Part of the Machine Learning / Artificial Intelligence Class Series. Optional: Attend 4 out of the 6 sessions and work towards obtaining a Technology Training ML/AI Proficiency Certification. 


  • 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.



  • 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.



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.



  • Because this is an abbreviated session, attendees MUST install Anaconda software 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.


University IT Technology Training classes are only available to Stanford University staff, faculty, students, and Stanford Hospitals & Clinics employees, including Stanford Health Care, Stanford Health Care Tri-Valley, Stanford Medicine Partners, and Stanford Medicine Children's Health. A valid SUNet ID is needed to enroll in a class.