Course overview
The Foundations of Artificial Intelligence course is a working introduction to how AI and machine learning actually function. It covers the data pipelines, model types, training process and evaluation methods that sit behind the tools in common use today, and it is honest about the limits — what these systems do well, where they fail, and the situations in which they should not be trusted. All work is done in Python on real datasets.
Who this course is for
This course is designed for the following groups. If you are not sure whether you fit, ask us — a short conversation is more useful than a longer guess.
- Developers and analysts who want to work with AI tools rather than just use them
- Technical professionals curious about what machine learning actually does
- Students preparing for further study or work in AI and data science
- Managers and leads who need a working understanding to make good decisions
- Security professionals interested in how AI is applied defensively
What you will learn
- What artificial intelligence is and, more importantly, what it is not
- How supervised, unsupervised and reinforcement learning differ in practice
- Preparing data: cleaning, feature selection and the mistakes that sink a model
- Training and evaluating a model on a real dataset in Python
- Reading model performance metrics and knowing which ones matter
- The failure modes: bias, overfitting, distribution shift and data leakage
- Where AI is used in industry today, including in security
- The ethical and practical questions that come with deploying a model
Curriculum
The course is taught in the module order below. Each module mixes explanation with hands-on work before moving on to the next.
- Introduction to AI
- Prompt Engineering
- AI Foundations
- AI Tools & Automation
- Python for AI Development
- AI for Business
- Data Analysis Fundamentals
- Responsible AI
- Machine Learning Fundamentals
- Generative AI
- Capstone Project
Tools and technologies
Everything used in the course is either free, open source, or available on a free tier suitable for learning. You will install and configure each of these during the course, not just watch someone else use them.
- Python 3
- Jupyter notebooks
- NumPy and pandas for data handling
- scikit-learn for classical machine learning
- Matplotlib for basic visualisation
- A small, public dataset for the end-to-end project
Prerequisites
We would rather you arrive prepared than arrive surprised. These are the things you should already be comfortable with before the first session.
- Some programming experience — Python preferred, any language accepted
- Comfort with the command line
- Basic school-level mathematics; the course explains the maths it uses
- No prior AI or machine learning experience required
Hands-on labs, CTFs and projects
Most of the course is spent on machines. The work below is what turns each topic from something you have read about into something you can actually do.
- Clean and prepare a real dataset, documenting every decision
- Train and evaluate a supervised learning model end to end
- Diagnose an overfitted model and fix it
- Investigate a biased dataset and describe the impact on outcomes
- Present a short, honest assessment of what a model can and cannot do
Learning outcomes
When you finish this course, these are the things you should be able to do without looking them up. Anything you cannot yet do, the trainer will help you with during the final project review.
- Explain in plain language what a machine learning model is doing
- Prepare data for training without introducing leakage or bias
- Train, evaluate and improve a simple model in Python
- Read and interpret common evaluation metrics
- Recognise the situations in which an AI system should not be trusted
- Discuss the ethical and practical trade-offs of deploying AI
Career paths
This course supports the roles below. We do not make placement claims or guarantee outcomes — what you do with the skills is up to you — but these are the job titles that most often ask for what this course teaches.
- Junior data scientist or machine learning engineer
- Data analyst with AI responsibilities
- AI-aware developer or product engineer
- Security analyst working with AI-assisted tooling
- Technical generalist in an AI-adjacent role
Frequently asked questions
School-level mathematics is enough to start. The course explains the small amount of linear algebra and statistics it actually uses, in plain terms, at the point where it comes up. If you can read a graph and understand an average, you have enough to begin.
It helps considerably. The course uses Python throughout, and while we do not spend time on syntax, you will need to be able to read and modify short scripts. If Python is new to you, our Python Development Professional course is the natural first step.
It will give you a working understanding of how machine learning is done and enough hands-on practice to work with models and data confidently. Becoming a data scientist requires more — statistics, larger projects and time — but this course is a solid foundation and a useful test of whether that path is right for you.
It covers the foundations on which modern language models are built and explains where they sit in the broader landscape. It does not attempt to teach you to train a large language model from scratch, which is not something any short course can honestly claim to do.
Yes, in both directions. AI is used defensively for anomaly detection and log analysis, and offensively to generate convincing phishing content and to probe for weaknesses. Understanding what these systems can and cannot do helps you reason about both.
