# Significance of Mean squared Error in Data Science! This blog aims to explain the need & logic behind using the Mean Squared Error in Machine Learning, Deep Learning, or Data Science. Anyone who has an intermediate level of knowledge in this field must have a doubt that why to use mean squared error, why not to use mean absolute error. This blog will explain the significance of Mean squared Error with deep Mathematical concepts!

Did you ever try to find the difference between Mean Squared Error(MSE) & *Mean Absolute Error(MAE), *rather than just the square term? It doesn’t matter the answer to this question is yes or no in your case, this blog will guide you from the right approach and significance of MSE in any field.

Most of the people think that the reason of using MSE is to remove the negative term in the output by squaring it, which is one of the correct reason, but it is not the major reason of using MSE, because negative term is also removed in the Mean Absolute Error by taking the absolute value of the output.

The actual reason behind using the Mean Squared Error is the concept of Maximum Likelihood Estimation(MLE).

There are chances that many people from the readers of this blog may know about MLE, but also, there will be some who are not aware of the concept. Therefore, I would like to explain this concept first, because this is the base & actual reason behind the usage of MSE.

### Maximum Likelihood Estimation

It is a simple process of maximizing our desired value in a probabilistic function through estimation, i.e. in a probabilistic function, we have to estimate the value of some parameters which will yield the maximum output of the probabilistic function which is our desire.

Note: In this significance of MSE, I am assuming the data distribution to be normal, data of discrete distribution, I will explain in my future blogs.

## Most popular Data Science and Machine Learning courses — July 2020

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

## PyTorch for Deep Learning | Data Science | Machine Learning | Python

PyTorch for Deep Learning | Data Science | Machine Learning | Python. PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides flexible tools for deep learning.

## Data Augmentation in Deep Learning | Data Science | Machine Learning

Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information from various sources to improve the quality of data of an organisation.

## Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.

## PyTorch for Deep Learning | Data Science | Machine Learning | Python

PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning.