I was reading the Hands on ML book and I'm on the SVM and Logistic Regression chapters. I started looking up more on these algorithms and apparently they are "linear" classifiers i.e the decision boundary is linear (The classifier needs the inputs to be linearly separable.)
Now in the book it is mentioned that since in most of the cases data is not linearly separable, we have to increase the dimensions of the features to make it linearly separable.
But is it always true that there is some transformation to convert every non-linearly separable data set into a linearly separable one? If not, what would be an example of such a data set where this is impossible?