AI Isn’t Magic — Here’s How It Learns

By Oluwole Akinwale, Manager, Professional Services

You’ve probably heard people talk about how “AI is learning,” but what does that really mean? How do machines learn in the first place?

The answer lies in something called machine learning — a process that teaches computers how to recognize patterns, make decisions, and even improve over time without the need to reprogram them from scratch.

Let’s break it down in everyday language.

What is Machine Learning?

Think of machine learning like training a dog. You don’t explain everything in words — you show, reward, and repeat. The dog picks up commands by recognizing patterns. Machine learning works the same way: we give a computer lots of examples (data), and it learns from them using algorithms (step-by-step instructions). Eventually, it gets better at noticing patterns as we feed it more data. It’s like when we say “practice makes perfect” – sounds familiar? Now let’s talk about the four main ways machines learn.

The 4 Main Ways Machines Learn

We all learn in our own way — some by reading, some by watching, and if you’re anything like me, by rolling up your sleeves and doing it. AI systems aren’t that different. We can train them in the following ways, depending on the task we want them to perform.

1. Supervised Learning

Imagine this as a teacher guiding their student by showing them example questions along with the correct answers. That’s how this type of machine learning works — we train the AI by feeding it lots of labelled examples, so it learns what’s right by recognizing patterns in those answers.

Imagine we want AI to recognize pictures of cats and dogs. We go through each photo and label it — “this is a cat,” “this is a dog”. We then feed thousands of those labelled images into the system. After seeing enough examples, the machine starts to figure out how to tell them apart all by itself.

2. Unsupervised Learning

Remember those times your parents or teachers let you figure things out on your own? I bet you hated it! This type of machine learning works similarly.

We give the AI lots of data, but no answers — it has to make sense of it by spotting patterns or groups. For example, we may think — how about we feed it thousands of customer shopping records without labels? It might naturally group customers based on their spending habits or preferences—a sure delight to our friends in marketing, and of course, anyone who uses recommendation systems.

3. Reinforcement Learning

As a dad of three, this one reminds me of the times my babies were learning to walk. I watched them fall several times, but each time, they adjusted and fell again before eventually getting it right.

It’s about experimenting, failing, and improving. Mistakes aren’t setbacks — they’re stepping stones to success. This isn’t a motivational piece, so let’s get back to AI. Reinforced Learning is similar to learning through trial and error, but with the addition of rewards and penalties. Think of a robot learning to bake a cake without a recipe.

The first time, it might add too much flour or forget the baking powder. The cake turns out dense or doesn’t rise. Next time, it adjusts — maybe a little less flour, a bit more sugar. With each attempt, it learns from what went wrong and get closer to the perfect cake.

4. Semi-supervised Learning

Hmm! This one is like your favorite mixed grill, but with just two items: steak and sausage, chicken and lamb chop, pork chop and bacon, or fish and prawn. In Semi-supervised Learning, the mix is Guidance and Guesswork.

This is where we employ the guidance of supervised learning in combination with the guesswork of unsupervised learning. We feed the system with some labelled data (with answers) and a larger amount of unlabeled data (without answers).

Imagine you’ve labelled 100 medical images as either “tumor” or “no tumor,” but you have 1,000 more without labels. AI can learn from the few labelled examples and then apply that learning to make sense of the rest.

As AI has matured, more specialized learning strategies have emerged, helping machines learn faster, smarter, and more ethically. I won’t bore you with the details.

Now, let’s conclude with why understanding machine learning is important.

Why Does It Matter?

Understanding how AI learns isn’t just tech talk — it impacts our everyday lives more than we realize:

Personalized experiences
From Netflix recommendations to your Spotify playlist, AI learns what you like and delivers more of it. I hope it’s more money for me. 🙂

Better healthcare
Imagine the difference it would make to our lives if we trained AI models on medical data so they can help detect diseases earlier and suggest more accurate treatments.

Smarter business decisions
As a business owner, I like to understand customer behavior, predict trends, and optimize operations. Sounds like a user story on a Jira board. Well, AI can help deliver that requirement, too.

Safer technology
In fields like self-driving cars or fraud detection, how an AI is trained determines how well it can spot danger or unusual activity.

Ethical responsibility
This is one I am passionate about. Understanding how AI is trained helps us ask the right questions about data privacy, fairness, and bias, which will impact us all.

In short: The better we understand how AI learns, the better we can shape how it works for us — and with us.