The 34 Days of Python series is moving into its second phase with Machine Learning. The journey will start with a first look at project setup and classification.

See how we can visual test the performance capability of our K-Nearest Neighbors classifier using Scikit Learn.

Continuing with our look into supervised learning, today we explore the basics linear regression by applying it to another Scikit Learn toy dataset.

In second blog post on linear regression, we take what we learned in part one and look deeper into the basics of linear regression and applying a train-test-split to our data.

In part three, we look at using k-fold cross-validation to prevent dependency on a particular train/test split.

In our final coverage of regression using SciKit Learn, we look at how we can use regression with regularization.