Machine Learning (ML) · Beginner level

Beginner level · Module 5 of 5 · 10 guided hours

Responsible ML and a First Project

Check data quality and bias, explain models to non-experts and complete the level project.

Learning objectives

  • Identify data quality problems and sources of bias
  • Explain a model's predictions and limits to non-experts
  • Complete and present a small, responsible ML project

Topics

  • representative data
  • sources of bias
  • comparing performance across groups
  • explaining models
  • human oversight
  • project presentation

Lessons

  1. Lesson 1 Data Quality and Bias

    Recognise how unrepresentative or flawed data leads to unfair or wrong models, and check performance across groups.

    representative samplehistorical biaslabel biasgroup-wise metrics

  2. Lesson 2 Explaining Models to Non-Experts

    Explain what a model does, how well it works and how it should — and should not — be used, in plain language.

    plain-language summaryfeature importanceuncertaintyhuman in the loop

  3. Lesson 3 Completing and Presenting the Project

    Finish the dropout-risk project, check it from start to finish and present it.

    reproducibilityrandom_statenotebook structurepresentation

Practical activity

Complete the dropout-risk level project, including a check of model performance for girls and boys and for rural and urban schools.

Expected outcomes

  • A complete small ML project
  • A habit of checking for bias and stating limits

Assessment

The level project assessed against its criteria, with a five-minute presentation to a 'head teacher'.

Free taster lesson Real-World ML: Ethics, Data Quality & Deployment Read a free self-study lesson on this topic in the Free Course Library.

Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.