Intermediate Python: Machine Learning
This four week course introduces participants to implementation of machine learning methods in Python using Jupyter Notebooks. Each two hour session will include brief tutorials 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 strong 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.
Much of the material for these lessons has been adapted from these sources:
Software requirements for this course can be found on fredhutch.io's Software page.
- Week 1: Conceptual Overview; CRISP-DM framework; EDA; Our Tools
- Week 2: Case Study in Regression
- Week 3: Case Study in Classification
- Week 4: Case Study in Deep Learning and Transfer Learning
- Each week's materials are described in the script prefaced with the number of the week.
exercises/includes a file for each week representing both the aggregated in-class exercises as well as additional supplemental exercises for practice
solutions/includes the solutions for all files in
instructors.mdincludes information for instructors to facilitate teaching each lesson
hackmdio.txtis an archive of the interactive webpage used during lessons