### A Simple Explanation of Regularization in Machine Learning

In this post, we are going to look into

Let's get started!!

The first Question always coming to my mind after hearing this term is:

The above definition gives three things to be looked into detail.

This model will not learn anything new but it can find a few patterns but not enough to predict the score. This is called the

**regularization**and also implement it from scratch in python (**Part02**). We will see with example and nice visuals to understand it in a much better way. We already know about the Linear regression where this is used.Let's get started!!

The first Question always coming to my mind after hearing this term is:

**What is regularisation?**This is a*(we will see what we mean by that) by penalizing the***technique to minimize the complexity of the model***.***loss function to solve overfitting**The above definition gives three things to be looked into detail.

**Minimize the complexity****Penalize the loss function****Solve the overfitting (Generalization).****1)Minimizing complexity. What do we mean by that?**Consider a simple example. you are trying to predict the score of students in the exam. We use a**number of books****read**as a**feature**to predict.This model will not learn anything new but it can find a few patterns but not enough to predict the score. This is called the

**underfitti…**

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