There are many interpretations and methods of normalization and the term is often used to describe standardization or rescaling so you'll need to specify what method you wish to use. Below I provide an example how to normalize columns of a matrix so that all values are between 0 and 1 according to the max and min within each column.
% fake data
data = randi(10, 6, 5);
% Subtract min of each column
datamin = data - min(data, [], 1);
% Scale to the max of each column
dataNorm = datamin ./ max(datamin, [], 1);
Now all values are of dataNorm are between 0 and 1 according to the max and min within each column. If this is what you meant by 'normalize', you can apply this to your data.
If you wanted to normalize via z-score, check out the normalize() function (from release 2018a). It also offers additional methods .
dataNorm = normalize(data, 1);
