Author(s): Aleti Adarsh Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Lets be honest machine learning looks like magic at first glance. You feed a model some data, and suddenly, it starts making predictions as if it has a crystal ball. But heres the secret: its not magic, its math.I remember when I first dipped my toes into machine learning. I was eager, excited, and confident until I hit a wall. That wall had a name: mathematics. It felt like an elite club that I wasnt invited to. Linear algebra? Sounded intimidating. Calculus? Flashbacks to high school nightmares. Probability? Well, lets just say my understanding of probability was a coin toss at best.If that sounds familiar, dont worry I got you. In this article, well break down the essential math concepts behind machine learning in a way that actually makes sense. No scary equations (okay, maybe a few, but I promise theyll be friendly). Think of this as a crash course in understanding why machine learning works under the hood.By the end of this, youll walk away with a solid intuition of linear algebra, calculus, and probability, and you might even find yourself enjoying math (I know, crazy, 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 asponsor. Published via Towards AI