Machine learning isn’t magic.
It’s geometry + feedback.

Models improve by measuring error, following the slope, and adjusting — step by step.

That’s where understanding begins.

#ai #machinelearning #computerscience

Include both parameters (w, b), and the cost becomes a surface.
A bowl.

Learning is no longer abstract math —
it’s moving downhill toward the lowest point.

#moving #include #ai

If we fix everything except the slope (w),
the cost becomes a function of w.

Plot it — and you get a U-shaped curve with a clear minimum.

#minimum #function #descent

The cost function combines all errors into one number:

J(w,b) = 1/(2m) Σ(ŷ − y)²

It answers one question:
How bad is this model overall?

#answer #combines #function #ai

Errors can be positive or negative.
To prevent them from canceling out, we square them.

This turns “being wrong” into a measurable penalty.

#turn #prevent #bewrong

For each data point, we compare prediction to reality:
ŷ − y

That difference is called error.
No error → no learning.

#called #education #learning #data

In linear regression, a model predicts using:
f(x) = wx + b

That’s just a line.
The real learning begins after the line makes mistakes.

#instamodels #mistakes #neverstoplearning

Machine learning doesn’t start with complex models.
It starts with a simple question:
How wrong was my prediction?

#start #education #learning #artificialintelligence

The intuition behind machine learning learning. 📚 🤖

#education #artificialintelligence #ai
https://iivanovlabs.com/understanding-the-cost-function-in-linear-regression/

This same idea scales up in modern AI systems:
learn from data → predict.

Linear regression isn’t about complexity.
It’s about building intuition — and realizing you can understand how intelligent systems learn.

#sameidea #linearregression #intelligentsystems #intelligentsystem #scaleup