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
Login or sign up now to access all of the sessions
- Session 1: Launch
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Session 2: Natural language processing
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Session 3: Recommendation systems
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Session 4: Decisions and ethics
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Session 5: Algorithms
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Session 6: Python and Orange
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Session 7: Screening ideas
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Session 8: Plan your model
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Session 9: Industry engagement session
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Session 10: Build and test your model
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Session 11: Pitch your model
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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
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Session 1
Launch
Learning objectives
Core
- Understand what machine learning is
- Be able to build a simple machine learning model
Challenge
- Be able to make changes to a simple machine learning model
Go to session
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Session 2
Natural language processing
Learning objectives
Core
- Understand what a chatbot is
- Be able to build a natural language digital assistant
Challenge
- Recognise the advantages of natural language processing
- Appreciate real life applications of chatbots
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Session 3
Recommendation systems
Learning objectives
Core
- Understand how machine learning can be used to make recommendations
- Be able to build a simple recommendations system
Challenge
- Be able to adapt a recommendation system to include new criteria
- Understand what a filter bubble is
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Session 4
Decisions and ethics
Learning objectives
Core
- Understand how machine learning is used to make decisions
- Understand potential issues with the application of machine learning
Challenge
- Be able to evaluate the impact of ethical considerations in the application of machine learning
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Session 5
Algorithms
Learning objectives
Core
- Understand the difference between supervised and unsupervised learning
- Understand basic machine learning algorithms
Challenge
- Be able to use basic machine learning algorithms
- Understand what a neural network is
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Session 6
Python and Orange
Learning objectives
Core
- Be able to use machine learning tools to visualise data
- Be able to use python to import data and run basic machine learning algorithms
Challenge
- Be able to apply learning to own datasets
This optional session assumes that students have a basic understanding of Python programming. If this does not apply, you can proceed to the next session.
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Session 7
Screening ideas
Learning objectives
Core
- Identify everyday problems which could be solved using machine learning
Challenge
- Be able to convey the key concepts of your idea in a 30 second pitch
- Be able to critically evaluate machine learning ideas and select the strongest to take forward
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Session 8
Plan your model
Learning objectives
Core
- Gain a better understanding of the data requirements of your machine learning product
- Gain a better understanding of who would use your machine learning product and how they would benefit
Challenge
- Be able to prepare data for a machine learning model
- Be able to design a machine learning model that will meet the needs of the user
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Session 9
Industry engagement session
Learning objectives
Core
- Gain information about the machine learning model development process from an industry Expert
- Be able to obtain constructive feedback on your machine learning ideas
Challenge
- Develop confidence discussing technical feasibility with an industry Expert
- Be able to act on constructive feedback to improve your machine learning model
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Session 10
Build and test your model
Learning objectives
Core
- Be able to develop and train your machine learning model
Challenge
- Be able to improve the accuracy of your model
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Session 11
Pitch your model
Learning objectives
Core
- Understand how to present your ideas effectively
Challenge
- Be able to summarise the key points of your machine learning model in a short presentation
- Understand different business models used for machine learning
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Session 12
Taking machine learning further
Learning obectives
Core
- Understand the range of jobs available developing machine learning
- Understand the impact of machine learning on all jobs
Challenge
- Understand the different routes into a career in machine learning