Machine Learning

Suitable for:
13 – 18 years old

24 – 36 hours

There is a steady flow of news announcing a new breakthrough or a new application built on top of Machine Learning (ML). The field is advanced enough to beat top human players in chess and Go, diagnose cancer better than doctors, and will soon be driving our cars with full autonomy. Elon Musk fears the domination of Artificial Intelligence (AI) that could lead to the rise of an immortal AI dictator, while others mock his prediction as far-fetched.

Yet, for most people, ML remains a black box: how does it work and what is it capable of doing? The math and algorithm behind ML is dense, requiring advanced study.

We have designed a course suitable for secondary school students to provide an intuitive understanding of ML. We minimise the math, and focus on hands-on exercises and explanations so that students can focus on understanding how ML works at a high level, and appreciate its potentials and limitations.

The course leverages on high-level machine learning libraries such as and scikit-learn to enable students to quickly prototype ML-based projects. They will build and deploy an image classification and natural language processing project.

What you’ll learn


By the end of this course, students will be able to:

  • explain how ML works at a high-level,
  • describe a typical ML workflow from data gathering to deployment,
  • use libraries to create an image classification ML project,
  • use libraries to create a natural language processing ML project, and
  • describe the potential and limitations of AI as well as understand the ethical issues that AI faces.

Learn for Life

  • Global awareness

Course Outline

Module 1 (20 hrs):

  • What is ML and AI?
  • Types of ML
  • Exploring ML workflow using IBM Watson
  • Regression
  • Neural Networks
  • Underfitting, Overfitting and Regularisation
  • Basic Python
  • Using to build an image classifier
  • Building a Pokemon classification web app and deploying it
  • Using to build a text classifier
  • Sentiment analysis on tweets
  • AI Ethics

What you’ll need


  • Google Colaboratory


  • Laptop computer
  • Internet access



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