Intermediate Python: Machine Learning
This four class course introduces participants to implementation of machine learning methods in Python using Jupyter Notebooks. Each two hour session will include brief tutorials and/or case studies interspersed with challenge exercises, and assumes attendees are familiar with basic Python syntax, using packages, and basic data manipulation using Pandas. The course also assumes a solid foundation in basic statistics as well as prior/concurrent participation in the fredhutch.io course Concepts in Machine Learning (or equivalent experience). At the end of this course, you will be able to apply basic principles of machine learning to research questions and will have established a foundation for further exploration of machine learning techniques.
Required software: You should come prepared with your own laptop with the software pre-installed to complete tutorials and challenges prior to the first day of class. Prior to the first class session, please ensure you can connect to the Marconi campus wireless network. Software requirements for this course can be found on fredhutch.io’s software page (http://www.fredhutch.io/software/). If you are an SCCA employee, please see the note at the bottom of the software page (http://www.fredhutch.io/software/). The HackMD (interactive page used for sharing links and information) for this course can be found here: https://hackmd.io/@k8hertweck/pythonML
- Class 1
- Class 2
- Class 3
- Class 4
Please see the following resources for more information on:
- Teaching these materials.
instructors.mdincludes information for instructors to facilitate teaching each lesson, including additional options if a participant can’t install R, RStudio, or tidyverse on their computer.
hackmdio.mdis an archive of the interactive webpage used during lessons.
- Contributing to lessons.
Each lesson’s materials are described in a Jupyter notebook (
*.ipynb) in the GitHub repository.
Much of the material for these lessons has been adapted from these sources as well as those referenced in specific notebooks: