Fundamentals

Machine learning is the process by which AI systems improve through experience. Instead of following hand-written rules, an ML model finds patterns in data. Here's how the cycle works:

The key ingredient is data. The more good-quality examples a model sees, the better it learns. This is why tech companies invest so heavily in collecting data — it's the raw material of AI.

Supervised learning is by far the most widely used form of ML today. The word "supervised" refers to the fact that a human has provided the correct answers — the labels — for every training example. The algorithm's job is to learn the mapping from inputs to those labels well enough to predict labels on new, unseen inputs.

Think of it like studying with an answer key. You practise on thousands of exam questions where you already know the correct answer. After enough practice you can answer new questions you've never seen before.

Labels, Features & Examples — The Core Vocabulary

Every supervised learning problem is built from the same three building blocks. Understanding these terms precisely will make every other concept in this course easier to follow:

Regression vs Classification

The two most common supervised learning task types — differing only in what kind of label you're predicting:

How the Supervision Signal Works

During training, the model makes a prediction on each labelled example. It then compares its prediction to the true label and receives a supervision signal — a measure of how wrong it was. This signal is used to adjust the model's internal parameters so the next prediction is a little better. Repeat this millions of times and the model gradually improves.

Real-World Supervised Learning Examples

When Supervised Learning Works Best

Supervised learning is a good fit when:

• Labels are available and reliableYou have historical data where the correct answer is known — past sales, medical records with confirmed diagnoses, emails users already tagged.
• The input-output relationship is stableThe patterns in training data will still hold when the model is deployed. A model trained on pre-2020 customer behaviour may not predict post-pandemic behaviour well.
• Enough labelled examples existSimple models may need thousands of examples; deep learning models may need millions. Rare events (rare diseases, rare fraud types) are harder to learn because labelled cases are scarce.
• Watch out: label leakageIf a feature in your training data accidentally encodes the answer (e.g., a "date of death" field in a mortality prediction model), the model learns a shortcut that won't exist at deployment time.

In unsupervised learning there are no labels. The algorithm is given raw data and asked to find structure on its own — groupings, patterns, compressions, or anomalies — without being told what to look for. This mirrors how humans often learn: by observing the world and finding categories without an explicit teacher.

Unsupervised learning is harder to evaluate (there's no "correct answer" to check against), but it's enormously useful when labelling is expensive, impossible, or when you want to discover something genuinely unknown in your data.

The Four Main Unsupervised Techniques

Dimensionality Reduction — Seeing the Invisible

Real datasets often have hundreds or thousands of features. Dimensionality reduction compresses this into 2–3 dimensions so humans (and other algorithms) can understand it. The key insight is that most high-dimensional data actually lies on a much lower-dimensional manifold — the apparent complexity is mostly redundancy.

AI systems are often categorised by their breadth — the range of tasks they can handle.

You interact with AI dozens of times every day — often without realising it. Here's a map of where AI shows up in a typical day:

AI systems are only as good as the data they're trained on and the goals they're given. Several well-documented failure modes deserve your attention as an AI user and citizen.

Bias in AI

If training data reflects historical inequalities, the model will perpetuate them. A famous example: early facial recognition systems had much higher error rates for darker skin tones because training datasets were skewed toward lighter-skinned faces.

Hallucinations

Large language models can generate confident-sounding but factually wrong information — a phenomenon called hallucination. They predict likely-sounding text, not necessarily true text. Always verify important claims from an AI chatbot using authoritative sources.

Privacy & Data

Many AI services are trained on or learn from user data. When you use a free AI product, consider: what data is collected, who owns it, how it's stored, and whether it's used to train future models.

Here are practical habits for anyone using AI tools today:

• Verify important outputsAI-generated text, code, medical or legal information should always be cross-checked. Use AI as a first draft or starting point, not a final source.
• Understand the limitsKnow what the AI you're using is designed to do. A language model is not a search engine; a recommendation system is not an objective advisor.
• Protect your dataDon't share sensitive personal information (passwords, financial details, medical records) with AI chatbots unless you've reviewed the platform's privacy policy.
• Watch for biasWhen using AI for hiring, healthcare, credit decisions, or any high-stakes domain, ask whether the system has been audited for bias and what redress exists if it makes a mistake.
• Be transparent about AI useIn academic, professional, or creative work, disclose when AI tools contributed substantially. This builds trust and supports honest norms around AI.
• Think about wider impactAI systems have environmental costs (energy for training), economic effects (job displacement), and societal implications. Informed citizens engage with these policy questions.

AI is developing faster than almost any technology in history. Here's a look at where things stand and where they're heading: