Image credit: nature

In AI, learning isn’t one-size-fits-all. Models learn from data in different ways, and understanding the three core types – supervised, unsupervised and deep learning (descriptive learning) – can help us better grasp how modern AI powers everyday applications, especially recommendation systems on platforms like Netflix and Amazon.

Supervised learning is like training with the teacher. The model is given labeled data, meaning it knows both the input and the correct output. For instance, if a Netflix user gives a 5 star rating to Gladiator movie, the system explicitly learns that the user prefers action or epic drama movies. This is known as content- based filtering, which relies on data like rating, watch time or purchase history. It works best when there’s structured data and clear feedback from users.

Unsupervised learning, by contrast, works without labels. The model looks for hidden patterns or grouping in the data on its own. A great example is collaborative filtering – used when two users with similar viewing habits get suggestions based on each other’s history, even if they never rated anything. So if you and another user watch similar types of shows, the system might recommend what the other watched to you. Netflix and Amazon use a blend of both methods to deliver smarter, more personalized recommendations.

Now let’s talk about deep learning, also called descriptive learning in some contexts. This method mimics how the human brain processes information by using neural networks. It’s especially powerful for task like image recognition, speech processing, and natural language understanding. For example, a voice assistant the understands spoken commands like “play something funny” doesn’t rely on user rating – it interprets tone, context and content to deliver the right experience. Deep learning models require large volumes of data and computing power, but they enable AI to learn complex patterns without being explicitly told what to look for.

To put it simply:

• Supervised learning says (labeled data), Here’s what the user liked – now find more like it.
• Unsupervised learning says (Unlabeled data) These users behave similarly – may be they like similar things.
• Deep learning says, I will analyze this complex data myself to make a smarter prediction.

Each type plays a unique role in how AI models understand, personalize, and improve the user experience we rely on every day.