both are used to avoid overfitting of data, while L1 estimates median of data whereas L2 estimates mean of the data.

The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while L2 regularization tries to estimate the mean of the data to avoid overfitting. Besides, a regression model that uses the L1 regularization technique is called Lasso Regression, and the model which uses L2 is called Ridge Regression.

You can see this for more details:

https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c

In addition to the above answers, both have different advantages. For example, the L2 regularization helps to decrease the cost function abruptly than L1 regularization due to the square term The main advantage of using L2 regularization is reflected when you use backpropagation or gradient descent, which may create a vanishing gradient for the L1 case.