What is machine learning?
Kahu team·Updated 2 Oct 2026·6 min read
Machine learning is the way most AI is built: instead of programming every rule, you give a computer lots of examples and it learns the patterns itself. Once trained, it can sort, predict or recommend on new data it has not seen before. Spam filters, fraud alerts and sales forecasts are common examples.
Rules versus learning
Imagine teaching software to spot a spam email. You could write rules: block anything with certain words, from certain addresses. Spammers change their wording and the rules break. With machine learning you show the software thousands of emails already marked spam or not spam, and it learns the patterns that separate them, including ones no person would have thought to write down.
| Rule-based software | Machine learning | |
|---|---|---|
| Who writes the logic | A programmer, rule by rule | Learned from examples |
| Handles new situations | Only if a rule covers them | Often, if they resemble the examples |
| Easy to explain | Yes, you can read the rules | Harder, the patterns are inside the model |
| Needs | Clear, stable rules | Plenty of good examples |
The three main types
- Supervised learning: learns from examples with the right answer attached, such as past sales labelled won or lost. Used for predictions and sorting.
- Unsupervised learning: finds groups and patterns in data with no labels, such as customers who buy in similar ways.
- Reinforcement learning: learns by trial and error, getting a reward for good outcomes. Used in games, robotics and to fine-tune chat assistants.
Where small businesses meet it
You rarely build machine learning yourself. It is built into the tools you already pay for. Your accounting software suggesting how to code a transaction, your email sorting promotions, an ad platform deciding who sees your ad, and a CRM scoring which leads look most promising are all machine learning at work.
What makes it work well
- 1Enough examplesA model learns from what it sees. A handful of records is not enough to find a reliable pattern.
- 2Clean, honest dataDuplicates, typos and missing fields teach it the wrong lessons.
- 3Data that matches todayA model trained on last year's customers may miss this year's changes.
- 4A person checking resultsReview its predictions against what actually happened, and correct it when it drifts.
Where Kahu fits
Two Thursday gaps at 11:00 and 14:30. Drafted messages to 6 waitlisted customers.
Updated the hours on your site and Google profile to Sat–Sun 8–2.
One post about the new hours, using your Sunday counter photo.Frequently asked questions
Is machine learning the same as AI?
It is the main way AI is built today, but AI is the wider field. All machine learning is AI, but not all AI is machine learning.
Do I need a data scientist to use it?
Not to use it. Most business tools include machine learning already. You need specialists only to build your own models.
How much data does machine learning need?
It depends on the task. Simple predictions can work with thousands of records, while large language models are trained on vastly more.