We discuss the possibility of the existence of an underlying fundamental relation between certain Machine Learning methods and the Renormalization Group flow in theoretical physics. We will start by introducing the basics of the restricted Boltzmann machines and their training methods. Then to establish the connection with the physics, we will be introducing lattice spin models. In particular we will use the Ising's model states for the training of the Boltzmann machines. We show that Boltzmann Machines identify spontaneously the phase transitions of the lattice spin models, with a process that turns out to resemble the Renormalization Group flow, but without having any direct knowledge about the Hamiltonian and the interactions of the physical model.