[D] Do I practice on one area of ML or is it better to practice on all algorithms?

I am an intermediate in ML and I know the theory of several supervised and unsupervised ML algorithms as well as deep learning. However, I lack huge amount of practical skills which is what I am working on right now. However, I am lost as to what to practice exactly. I am mostly interested in deep learning but I am seeing how essential it is to know how to implement other algorithms as well like random forests, SVM.. etc. Do I practice DL and other ML algorithms simultaneously (as mastering one area is almost impossible and is done across many years of experience) or should I first focus on one area (say computer vision) and then move on to the rest?