06-linear-regression-vector.md

2.6 Linear regression: vector form

Slides

Notes

The formula of linear regression can be synthesized with the dot product between features and weights. The feature vector includes the bias term with an x value of one, such as $w_{0}^{x_{i0}},\ where\ x_{i0} = 1\ for\ w_0$.

When all the records are included, the linear regression can be calculated with the dot product between feature matrix and vector of weights, obtaining the y vector of predictions.

The entire code of this project is available in this jupyter notebook.

⚠️	The notes are written by the community. If you see an error here, please create a PR with a fix.

• Notes from Peter Ernicke

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