Machine Learning Beta
Create a machine learning prototype in 12 sessions
Primary, secondary of FE
10 to 12 hours
In-class or extracurricular
In-class or extracurricular
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 10 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: Course 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: Request an Expert
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Session 10: Build and test your MVP
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Session 11: Pitch your MVP
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Session 12: Go Further
Core resource
Course workbook for students
- This printable student A4 workbook contains 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
Course Launch
Core Learning Objectives
- 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
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Session 2
Natural Language Processing
Core Learning Objectives
- Understand what a chatbot is
- Be able to build a natural language digital assistant
Challenge
- Recognize the advantages of natural language processing
- Appreciate real life applications of chatbots
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Session 3
Recommendation Systems
Core Learning Objectives
- 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
Core Learning Objectives
- 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
Core Learning Objectives
- 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
Core Learning Objectives
- 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
Core Learning Objectives
- 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
Core Learning Objectives
- 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
Request an Expert
Core Learning Objectives
- 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 MVP
Core Learning Objective
- 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 MVP
Core Learning Objective
- 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
Go Further
Core Learning Objectives
- 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