The Math Behind Supervised Learning: Making AI Less Mysterious

Author(s): Aleti Adarsh

Originally published on Towards AI.

Ever Wondered How AI ‘Learns’? Let’s Break It Down!

This member-only story is on us. Upgrade to access all of Medium.

Have you ever looked at AI models and thought, “How the heck does this thing actually learn?” You’re not alone. Supervised learning, a cornerstone of machine learning, often seems like magic — like feeding a computer some data and watching it miraculously predict things. But beneath the surface, it’s all math — yes, the stuff many of us feared in school!

If the mere mention of linear algebra and probability makes you want to run for the hills, stick around. I promise I’ll make it painless (and maybe even fun!). Think of this as a friendly chat where we untangle the numbers that power AI — because if you understand the math, you unlock the real secrets of machine learning.

You can read this article for free :- Link

Before we dive into the numbers, let’s set the stage. Supervised learning is like training a pet — you reward it when it gets something right and correct it when it messes up.

Imagine teaching a toddler to recognize apples and oranges. You show them a picture of an apple and say, “This is an apple.” Then you show an orange and say, “This… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI