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Machine Learning/AI Series: Optimizing Machine Learning Models

New session times will be displayed below upon confirmation.

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. 


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.

 

Custom training workshops are available for this program

Technology training sessions structured around individual or group learning objectives. Learn more about custom training

University IT Technology Training sessions are available to a wide range of participants, including Stanford University staff, faculty, students, and employees of Stanford Hospitals & Clinics, such as Stanford Health Care, Stanford Health Care Tri-Valley, Stanford Medicine Partners, and Stanford Medicine Children's Health.

Additionally, some of these programs are open to interested individuals not affiliated with Stanford, allowing for broader community engagement and learning opportunities.