It looks like you are using RapidMiner documentation as a medium to study. If that is case, then the best answer to your question and probably to some other questions of yours should be, I believe, a recommendation of related textbook, tutorial or lecture notes.
For the current question, I recommend you to read a tutorial on Principal Components Analysis by Lindsay I Smith, a tutorial that is self-contained and easy to read.
Now let me get back to you question, assuming we have gone through that tutorial up to and including section 3.1 or any equivalent materials.
What is meant by transforming "correlated attributes into a set of values of uncorrelated attributes" in Principal Component Analysis"?
That just means, given existing $x$ axis and $y$ axis, and a set of data points with their coordinates (a set of observations of possibly correlated attributes), PCA will produce their eigenvectors and, using these eigenvectors as the new $x$ axis and $y$ axis as well as moving the origin to the center of the data points, obtain the new coordinates of those data points (principal components). The crux of the PCA is that the new $x$ coordinates and the new $y$ coordinates of the data points are 2 uncorrelated variables (uncorrelated attributes) in the sense that their covariance is 0. I am talking about 2-dimensional data for simplicity; PCA applies to 3 or more dimension the same way. In higher dimensions, the new coordinates of the data points are a set of variables every pair between which are uncorrelated.
All these concepts become very much alive and easy to understand once you have gone through several end-to-end computations. That is probably the fastest way to understand these concepts.