How Do Machines Learn?
What AI is, how it differs from ordinary software, what a neuron does, and how a network learns from its mistakes.
Modules
Each module explains one idea in plain words, lets you try it, then checks what stuck.
- 01
The Big Question: What Is Artificial Intelligence?
Tell AI apart from ordinary computing: AI makes judgment calls on tasks that have no exact recipe.
- 02
Traditional Software vs AI: What's the Difference?
Understand the big difference between AI and normal software: AI learns its rules from examples.
- 03
Machine Learning, Deep Learning and LLMs: The AI Family Tree
Know what AI, machine learning, deep learning and LLMs mean, and how they fit inside each other.
- 04
Supervised vs Unsupervised vs Reinforcement Learning
Tell apart the three ways a machine can learn: from answers, from raw data with no answers, and from rewards.
- 05
Where Does AI Get Its Training Data?
Know where the answers in training data come from, and why their quality caps how good a model can be.
- 06
How Machines Discover Patterns in Data
See how a model finds a pattern in data and uses it to make predictions, and why more examples help.
- 07
Meet the Artificial Neuron
See that a neuron just multiplies its inputs by weights, adds them up and adds a bias.
- 08
How a Neuron Makes a Decision
See how a neuron turns its total into the signal it passes on, and why that last bend matters so much.
- 09
Connecting Neurons: Building a Neural Network
See why networks need hidden layers, and how stacking layers makes them deep.
- 10
How AI Learns From Its Mistakes
See how a model turns “how wrong was I?” into a single number it can push down: the loss.
- 11
Gradient Descent: Finding Better Weights
Understand how a network finds good weights by itself: measure the mistakes, then nudge the weights downhill.
- 12
Backpropagation: How Neural Networks Know What to Change
See how a network works out, for every weight at once, how much it was to blame for a mistake.
- 13
Training vs Inference: Learning vs Using
Know the two jobs of every model, training and inference, and why both need huge amounts of math.
- 14
When AI Memorizes Instead of Learning
Spot overfitting: a model that aces its training examples by memorising them, then flops on anything new.
- 15
Testing AI on Data It Has Never Seen
Know why models are judged on data they never trained on, and how a leak can fake a great score.
- 16
Why Better Data Can Beat a Bigger Model
See why clean, representative data often matters more than a bigger model.
- 17
What Does an AI Model Actually Remember?
Understand that a trained model keeps patterns, not its training data, and why that makes its memory blurry.
- 18
Why Did AI Suddenly Get So Good?
Understand the three ingredients that came together around 2012 and set off the AI boom.
- 19
From Neural Networks to Language Models
See that a language model is the same machine you just learned, trained to predict the next word.