We propose a new correction scheme using a deep neural network with unsupervised learning to correct Nyquist ghosts and geometry distortions occurring in EPI. The proposed scheme includes NGAC-net and GDC-net. First, the NGAC-net estimates the phase error of k-space with the help of a ghost formulation operator and correlation loss. The NGAC-net produces two Nyquist ghost corrected images obtained by dual-polarity phase-encoding gradients. The GDC-net is trained to estimate the frequency-shift map using the two output images from the NGAC-net. Afterwards, an MR image generation operator utilizes the estimated frequency-shift map to obtain the geometry distortion corrected images.