Beginner 14 hours 13 lessons
AI Fundamentals
From zero to a working mental model of modern AI
Build a rock-solid intuition for how modern AI actually works — no PhD required. Learn the vocabulary, the building blocks, and ship your first model.
Learning objectives
By the end of this course, you'll be able to:
- Explain how machine learning differs from traditional programming
- Read and reason about a neural network diagram with confidence
- Train, evaluate and interpret your first classification model
- Recognise where AI helps — and where it quietly fails
What you'll build
Project 01
Handwritten Digit Recogniser
Train a small neural network to read handwritten numbers and watch accuracy climb in real time.
Project 02
Sentiment Classifier
Build a model that tells whether a movie review is positive or negative, then probe why it gets things wrong.
Syllabus
4 modules · 13 lessons
- 01
Foundations
- What intelligence means for a machine
- Data, features and labels
- The learning loop: predict, measure, adjust
- 02
Models & Training
- Linear models and decision boundaries
- Loss functions in plain English
- Gradient descent, visualised
- Overfitting and how to spot it
- 03
Neural Networks
- From neuron to network
- Activation functions
- Backpropagation without the scary math
- 04
Putting It To Work
- Evaluating a model honestly
- Bias, fairness and failure modes
- Capstone: ship a model others can use
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