The Math Behind Machine Learning: Linear Algebra, Calculus & Probability

Author(s): Aleti Adarsh

Originally published on Towards AI.

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Let’s 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 here’s the secret: it’s not magic, it’s 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 wasn’t invited to. Linear algebra? Sounded intimidating. Calculus? Flashbacks to high school nightmares. Probability? Well, let’s just say my understanding of probability was a coin toss at best.

If that sounds familiar, don’t worry — I got you. In this article, we’ll 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 they’ll be friendly). Think of this as a crash course in understanding why machine learning works under the hood.

By the end of this, you’ll 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.

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Published via Towards AI