The Ultimate Guide to Linear Regression

Linear regression is one of the most fundamental algorithms for data scientists. I also find it is extremely common ground for interview questions.

Yet - I find it is also shallowly understood.

Don’t be the person who when asked about linear regression only knows how to call fit() and predict() in scikit-learn.

To help you out, I have assembled the Ultimate Guide to Linear Regression. This article will cover:

• How to mathematically derive linear regression for gradient descent

• Show you how to code a simple example from scratch

• Compare with scikit-learn’s model

• Show you how to interpret your results

• Explore how to generate confidence intervals

• And how to tune your algorithm depending on whether you have high bias or variance

This is by far the most in-depth article I have written. If you enjoy it, please share it on sites like LinkedIn and Twitter and tag me! This helps me know what content is most valuable.

Here is the direct link to the article:

https://learningwithdata.com/posts/tylerfolkman/the-ultimate-guide-to-linear-regression/

All the best,

Tyler