I love it since it has actual C code for implementing the ideas. You get the intuition for the algorithms, how that intuition translates into pseudocode, and how that pseudocode becomes real C code.
Another lesson that it uniquely provides is that in day to day life you don't use these algorithms. You create new algorithms using the ideas behind the famous ones as a guide. An algorithms book or class that doesn't teach you that problem-solving mindset is basically useless.
Yes, we mostly study the famous algorithms the same way mathematicians study the famous theorems: not because they want to reprove them again and again in daily life.
Mathematicians look for more elegant proofs of already proven theorems all the time. The earliest proof is usually not the simplest, nor the clearest, nor the easiest to generalize, nor <insert desideratum here>.
Similarly, I think there is scientific value in finding cleaner ways to implement known algorithms, prove their complexity bounds, etc. For example, one of my current projects is to provide total functional implementations of known data structures and algorithms (i.e., free of assertions and unreachable control flow points), and I have gained insight about these data structures and algorithms that I couldn't have possibly obtained from their published descriptions.
Another lesson that it uniquely provides is that in day to day life you don't use these algorithms. You create new algorithms using the ideas behind the famous ones as a guide. An algorithms book or class that doesn't teach you that problem-solving mindset is basically useless.