Learning curves are plots used to show a model's performance as the training set size increases. Another way it can be used is to show the model's performance over a defined period of time. We typically used them to diagnose algorithms that learn incrementally from data. It works by evaluating a model on the training and validation datasets, then plotting the measured performance.

From the articles of geeksforgeeks.org and javatpoint.com we have a look at overall information about regularization and it's types. After that we have a visual explanations in videos of the YouTube channel StatQuest with Josh Starmer. At the end we explore the article published in Towards Data Science by Prashant Gupta.

Let's have a quick overall information given in one of the articles of javatpoint.com, after which we have a video from StatQuest with Josh Starmer, then we will go through article with detailed overview published in Towards Data Science by Saishruthi Swaminathan.

We begin to dive into the world of actual Machine Learning starting from this topic. There are few techniques of ML: Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning. And Linear Regression is one of the bases of "Supervised Learning" techniques.

So, we started to explore Supervised Learning. Let's have a bit understanding of approximating a target function (f) that maps input variables (X) to an output variable (Y)

The article published in Towards Data Science by Andrew Engel explains clearly about Categorical Variables for Machine Learning Algorithms. I advice you to visit and go over eyes this article