Hi Dev, to answer your question

Linear Regression and Logistic Regression are both Supervised Machine Learning Algorithms.

The basic difference between the Logistic and Linear Regression are :

1) Prediction

Linear Regression is used for making predictions when the outcome is continuous in nature.

Eg. Predicting price of the house based on several features.

Logistic regression is used for making predictions when the outcome is nominal or categorical in nature.

Eg. Predicting whether a person has diabetes or not thus classifying into Yes for having diabetes and No for not having diabetes.

2) Assumptions

Linear regression assumes a linear relation between an independent variable and dependent variable.Linear Regression is also sensitive to outliers.

Logistic regression on the other hand does not require a linear relationship between dependent and independent variables.

Because the outliers are tapered by the sigmoid function, outliers have little impact on logistic regression models. However, the presence of severe outliers may have an impact on the model's performance, decreasing it.

3) Equation

Linear regression equation is given simply by:

Y = mX + C,

Logistic regression equation is given by:

Y = eX + e-X

4) Way of reducing errors is different

Linear regression uses ordinary least squares to minimize error and find the best fit for the model.

Logistic regression uses a maximum likelihood method to minimize the errors.

Hope this helps.