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Introduction

In this article, we will look at the Linear Regression model for Machine Learning, which is one of the most basic models available.

Linear Regression

This equation shows a multi-dimension formula for linear regression, where ŷ is the predicted multidimensional value, n is the number of dimensions (or commonly called features),xi is the ith feature value, and ⍬ is the jth model parameter or weight.

With this definition, we now must see the way to train a model following this equation. Training a model means setting its parameters so the model best fits the training set, also we must find a measure of how well the model fits the training data, for this purpose we could use the Mean Square Error.

Here we are using x, y, ⍬ as vectors of size m.

Taking this into account, to train a

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