Artificial Intelligence Foundations

About this course

AI is changing how work gets done, and you do not need to be a data scientist to use it well. This course explains where AI came from and the key terms behind it, shows how industries apply it, introduces generative AI and the planning that a responsible AI project needs, and covers ethics, bias and careers in AI.

What you will be able to do

• By the end you can explain what AI is, how the field developed, and the key terms machine learning, deep learning and neural networks.
• By the end you can describe practical AI uses across industries, from healthcare and finance to manufacturing and agriculture.
• By the end you can explain generative AI, large language models and prompting, and outline a sensible AI project plan.
• By the end you can discuss AI ethics, bias and fairness, and identify the skills and career paths in AI.

Course outline

1. What AI is and where it came from

   11 min · Lesson notes, example, action task · 2-question quiz

   AI has moved through periods of optimism and setbacks since it was first named as a field. Knowing that history, and the core terms, helps you separate realistic AI use from hype.
2. AI in industries and everyday life

   10 min · Lesson notes, example, action task · 2-question quiz

   AI is already used across industries, from diagnosing disease and detecting fraud to predictive maintenance and precision farming. Recognising common uses helps you spot where AI can help and where it creates risk.
3. Generative AI, prompts and large language models

   11 min · Lesson notes, example, action task · 2-question quiz

   Generative AI creates new text, images and code from patterns learnt in data. Large language models power many of these tools, and the way you write prompts shapes what you get back. Outputs always need checking.
4. Planning and implementing an AI solution

   11 min · Lesson notes, example, action task · 2-question quiz

   A good AI project starts with a business problem and clear success measures, not with a tool. Good data, sensible tool choices and testing after launch matter more than the technology itself.
5. AI ethics, bias and fairness

   9 min · Lesson notes, example, action task · 2-question quiz

   AI systems can treat people unfairly if bias enters through the data, the design or the way they are used. Ethics frameworks, fairness measures, transparency and privacy protections help teams build systems that people can trust and challenge.
6. Careers in AI

   8 min · Lesson notes, example, action task · 2-question quiz

   AI careers draw on machine learning, programming and data skills, but many roles also need clear communication and a sense of the business problem. A small, well-documented project is often the best way to show what you can do.
7. Final quiz and certificate

   10 questions, pass mark 8 of 10. Pass to get your Reesby Academy certificate with a verification link.