AI Foundations
For someone who can write a little code and wants the concepts underneath machine learning to stop being vocabulary.
- Data, labels and features
- Training and evaluation
- First classifier, built end to end
- Topic quizzes and glossary
A mobile learning course — studied wherever the hardware is
Learn the fundamentals of artificial intelligence and robotics engineering in order: concept, worked example, build, quiz.
Sensors before control loops, vectors and matrices before neural networks. Each topic assumes only what the previous one taught, so nothing starts with a framework you have never seen.
Wiring diagram, parts list, the code, and the checks that tell you whether it worked. Line follower, obstacle-avoidance rover, servo arm, sensor logger.
Short checks at the end of each topic. A wrong choice returns the reason it is wrong and the lesson paragraph it came from, rather than just a score.
PWM, inverse kinematics, overfitting, odometry, loss function — one clear paragraph each, reachable from the line of the lesson that used the term.
See which topics are finished, which quizzes are worth retaking and where you stopped last time, across both the AI and the robotics side.
Screen in one hand, breadboard on the desk — this is the course being used, not a product shot.
Pick the one that matches where you are. Tell us in the form which it is and we will reply with the starting point.
For someone who can write a little code and wants the concepts underneath machine learning to stop being vocabulary.
For someone starting at the bench: a board, a sensor, a motor, and the wiring that makes them one machine.
The project half on its own, for learners who already have the theory and want guided builds to finish.
For teachers and club leads running a group through the same material at the same pace.
Tracks are arranged through the enquiry form — say whether you are learning on your own or running a group, and what hardware you already own.
Name, email, and one line about what you already know — school physics, some Python, or nothing yet. All three are fine starting points.
We reply with the track and the first topic, and say plainly whether you need hardware for it or whether the first weeks run on the phone alone.
Lesson, worked example, quiz. Progress is saved per topic, so a week away does not mean starting the chapter again.
Each block closes with a project you can see move or classify. If it does not work, the guide lists what to measure first.
This is written for two people. The first is a student — secondary school or early university — who has an AI or robotics module ahead and wants the ideas in plain order before the exam vocabulary arrives. The second is a hobbyist with a drawer of boards and sensors who can make things blink but has never had anyone explain why the control loop oscillates.
Both get the same treatment. Nothing is introduced with a metaphor that falls apart two paragraphs later, and nothing is skipped because it looks like maths. Where a formula is needed, it is written out and then used immediately on a number from one of the projects, so it has somewhere to land.
The pacing assumes short sessions. A topic is sized to be finished in one sitting on a phone, with the build left for a desk and a free evening. Progress tracking exists for that reason — it is there so you can stop in the middle of a term and come back knowing exactly which sensor chapter you never finished.
If you are not sure which track fits, say so in the enquiry. Describing what you have already built, or that you have built nothing, is the most useful thing you can put in the message.
Tell us where you are starting from and we will come back with the track, the first topic and the hardware — if any — that it needs.
Thank you — someone from the team will get in touch shortly with the track that fits and where to start.
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