Machine Learning Standard
Create a machine learning prototype in 10 sessions
- Students explore a wide range of machine learning applications and assess the social, legal and ethical impact of the use of AI algorithms
- Student work in teams or individually to design and build a prototype that solves a problem they care about using machine learning altorithms
- Students work their way through a range of activities, split across 10-15 sessions
- See below for the scheme of work, student workbook, and learning objectives
Secondary and FE
10-11 Sessions
In-class or extracurricular
Basic programming
Machine Learning Standard 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 adopt to meet your needs.
Scheme of Work
- Get a quick overview of the course structure
- Review the learning objectives and otucomes for each session
Expert Feedback Tool
This video explains how to use our brand new Expert Feedback tool to obtain personalised feedback on your project from our Industry Expert volunteers.
Course sessions
Login or sign up now to access all of the sessions
Session 1: What is machine learning?
Objective: To understand what machine learning is
Session 2: Facial recognition
Objective: To understand how facial recognition works
Session 3: Natural Language Processing
Objective: To understand what a chatbot is
Session 4: Recommendation Systems
Objective: To understand how machine learning can be used to make recommendations
Session 5: Decisions and Ethics
Objective: To understand how machine learning is used to make decisions
Session 6: Putting It All Together
Objective: To understand the potential impact of machine learning on employment and careers
Session 7: Spotting problems
Objective: To understand how to identify everyday problems which could be solved using machine learning
Session 8: Plan your model
Objective: To gain a better understanding of the data requirements of your machine learning idea
Session 9: Industry Engagement Session (Optional)
Objective: To gain information about the machine learning model development process from an industry Expert
Session 10: Build and Test Model
Objective: To be able to develop and train your machine learning model
Session 11: Pitch your model
Objective: To understand how to present your ideas effectively
Session 12: Algorithms (optional)
Objective: To understand the difference between supervised and unsupervised learning
Session 13: Using Python and Orange (Optional)
Objective: To be able to use machine learning tools to visualise data
Session 14: Careers in Machine Learning (Optional)
Objective: To understand the range of jobs available developing machine learning