Machine Learning

Create a machine learning prototype in 12 sessions

Primary, secondary of FE

12 sessions

In-class or extracurricular

Basic to advanced programming

Course Summary

  • Student teams design and build a prototype that solves a problem they care about using machine learning altorithms
  • Teams work their way through a range of activities, split across 12 sessions
  • See below for the scheme of work, student workbook, and learning objectives

Course sessions

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  1. Session 1: Launch
  2. Session 2: Natural language processing

  3. Session 3: Recommendation systems

  4. Session 4: Decisions and ethics

  5. Session 5: Algorithms

  6. Session 6: Python and Orange

  7. Session 7: Screening ideas

  8. Session 8: Plan your model

  9. Session 9: Industry engagement session

  10. Session 10: Build and test your model

  11. Session 11: Pitch your model

  12. Session 12: Taking machine learning further

Core resource

Course workbook for students

  • Printable student A4 workbook containing practical activities
  • Guides you and your students through the course
  • Fully editable, making it easy for you to adapt to meet your needs

Core resource

Scheme of work

  • Get a quick overview of the course structure
  • Review the learning objectives and outcomes for each session