
On the earth of knowledge and computer programs, the concept of Machine Learning might sound like a troublesome nut to crack, filled with tricky math and sophisticated ideas.
For this reason today I need to decelerate and take a look at the fundamental 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 straightforward, yet super-important, models which are the ABCs of ML. Consider it as starting with the simple pieces of an enormous puzzle. We’re going back to the straightforward stuff, where it’s easy to get what’s occurring.
So come along for the ride as we break it down and make all of it clear.
Let’s dive into Easy Linear Regression, step-by-step, together! 👇🏻🤓
The realm of predictive evaluation is vast, yet at its heart lies Linear Regression — the only method to make sense of knowledge trends.
While its extensions into multiple variables can seem daunting, our focus today narrows right down to Easy Linear Regression.
🎯 The important goal?
Discover 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 massive idea is to see how one thing can predict the opposite.
Sounds interesting, right?
So now… let’s attempt to make some sense of Linear Regression wondering…
Consider it as a team effort where two things work together: