Machine Learning with Python

Implement basic machine learning models for small to large datasets in Python.

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Class information

  • Dates: Spring 2026: April 29, May 6, 13, 20, 27, June 3

  • Time: Wednesdays Noon - 1:30 pm PT

  • Time commitment: 6 weeks of 1.5 hour classes and 1-2 hours of practice outside class.

  • Audience: The course is intended for researchers who want to implement basic machine learning models for small to large datasets in Python

  • Prerequisites: Intro to Python

  • Followed by: None

Learning Objectives (LOs)

  • Compare machine learning models in terms of flexibility vs. Interpretability.

  • Compare machine learning model performance in terms of bias and variance trade-off.

  • Implement and Interpret models such as linear regression, logistic regression, and lasso using a Tidy dataset via existing packages such as Sklearn and Statsmodels.

  • Evaluate model performance metrics for inference and prediction, such as AIC, BIC, MSE, and AUC, under a cross validated framework if appropriate.

  • Explain the difference in machine learning techniques between low and high dimensional data.

Frequently Asked Questions

  • How often is this class taught?

    This class is offered once a year. Check the schedule to see when it will next be offered.

  • Can I work through the materials without taking the class?

    Yes, you can access the course materials on your own any time.

  • Can I get a badge without taking the class?

    You need to complete the live class to get a badge.

  • Can I take the class if I’m not an employee at Fred Hutch?

    The class is open to members of the Cancer Consortium including Fred Hutch, the University of Washington, and Seattle Children’s Hospital.

  • Can I take more than one class at once?

    You can, but we recommend you don’t mix R and Python at the same time, because it can be confusing to switch between the two.

  • Can I take this class more than once?

    You can sign up more than once, but be aware that there is tyspically a waitlist. We will prioritize students who have not taken the class. If there is room you are welcome to take it again.