# What is the difference between Linear SVM and SVM with linear kernel?

I'm wondering whether there is a difference between Linear SVM and SVM with a linear kernel. Or is a linear SVM just a SVM with a linear kernel?

If so, what is the difference between the two variables linear_svm and linear_kernel in the following code.

from sklearn import svm
linear_svm = svm.LinearSVC(C=1).fit(X_train, y_train)
linear_kernel_svm=svm.SVC(kernel='linear', C=1).fit(X_train, y_train)