In the world of data and computer programs, the concept of Machine Learning might sound like a tough nut to crack, full of tricky math and complex ideas.
This is why today I want to slow down and check out the basic stuff that makes all this work. I’m kicking off a fresh set of articles I’m calling MLBasics.
We’re going to revisit the simple, yet super-important, models that are the ABCs of ML. Think of it as starting with the easy pieces of a big puzzle. We’re going back to the simple stuff, where it’s easy to get what’s going on.
So come along for the ride as we break it down and make it all clear.
Let’s dive into Simple Linear Regression, step by step, together! 👇🏻🤓
The realm of predictive analysis is vast, yet at its heart lies Linear Regression — the simplest method to make sense of data trends.
While its extensions into multiple variables can seem daunting, our focus today narrows down to Simple Linear Regression.
🎯 The main goal?
Find a linear relationship between:
- The independent variable or predictor.
- The dependent variable or output
In plain talk, Linear Regression is all about finding a straight line that shows how two things are connected — like how much you study (that’s the independent bit) and your test scores (that’s the dependent bit).
The big idea is to see how one thing can predict the other.
Sounds interesting, right?
So now… let’s try to make some sense of Linear Regression wondering…
Think of it as a team effort where two things work together:
