Machine Learning is the science of teaching machines to do better than plain, hard-coded programs.
Instead of telling a computer exactly what to do at every step, we let it learn from data. These learning algorithms help machines spot patterns, understand the mathematical relationships behind those patterns, and use them to make predictions on new, unseen inputs.
Sounds interesting, right?
Curious about how a machine actually learns all this? 👀
This guide will walk you through one of the simplest — yet most powerful — concepts in machine learning: Simple Linear Regression.
What is Linear Regression?
Linear Regression is an algorithm that helps us fit a straight line to a given set of data.
Take a look at the image below. You’ll notice several data points scattered across the graph. Technically, you could draw many different lines through these points — but why do we choose this one specific line?
That’s exactly what the machine learning algorithm figures out.
The goal is to find a line that predicts values as close as possible to the actual data points. In simple terms, the algorithm fits the line in such a way that the overall prediction error is minimized — and this condition holds true for all the data points.
Now that we know we’re dealing with a line, what do you think the algorithm is actually trying to predict?
Exactly — it tries to learn a function f(x) that represents a straight line.
And if my Indian folks here have survived 11th and 12th grade math, you already know where this is going 😄
Yep, we’re predicting the equation of a line.
How do we predict this line?
To do that, we need to understand two important things:
- The Cost Function
- Gradient Descent
These two work together to help the algorithm figure out how good a line is and how to improve it step by step.
In the next part of this guide, we’ll dive into the cost function — where we’ll see how the algorithm measures error and how it tries to fit the best possible line to the data.
More math, more intuition, and less confusion 😉
Stay tuned.
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