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Machine Learning/AI Series: Classification Algorithms

New session times will be displayed below upon confirmation.

Most Technology Training classes will be delivered online until further notice.

Before each sesson, Tech Training will provide a Zoom link for live online classes, along with any required class materials.


A session intended to get you started with Classification Algorithms to classify structured data and make predictions.

Have a basic understanding of the Python language, Pandas library, and be familiar with how to use Jupyter Notebook.

This session is designed for anyone who is familiar with the basic steps involved in machine learning, and the tools involved in building machine learning models.

Learn about what classification means in machine learning and some of the algorithms involved with classification. We will then perform hands-on labs to do the following:

  • Use one of these classification algorithms to build a classification model
  • Look at different metrics involved in evaluating the performance of the model

Attendees must install Anaconda software ( prior to the class, and have a basic understanding of using Jupyter Notebook.

Antony Ross

Antony originally attained a degree in psychology with an emphasis in sport psychology. He began working with athletes and eventually chose to pursue a graduate degree in exercise physiology. He conducted research in muscle physiology while teaching at USC and, subsequently, UCLA.

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.