Data transfer across DRAM channels accounts for nearly a quarter of the total energy consumption of DDR4 DRAMs. Modern applications with high bandwidth requirements further increase channel energy consumption. However, channel energy consumption is dependent on data being transferred. Pseudo Open Drain (POD) asymmetric termination, used in current DDR4 systems, consumes energy only when 1's are being transmitted over the channels. Many modern applications, including AI/ML ones are resilient to errors in data, and can work well with approximate data. This resilience can vary widely across and within applications, which provides a number of ways for exploiting these characteristics to save data transfer energy across the DRAM channel. However, all DRAM data encoding schemes have been targeted towards applications that require exact data and are not approximation resilient. In this paper, we propose Zero Aware Configurable Data Encoding by Skipping Transfer (ZAC-DEST), a data encoding scheme to reduce the energy consumption of DRAM channels, specifically targeted towards approximate computing and error resilient applications. ZAC-DEST exploits the similarity between recent data transfers across channels and information about error resilience behaviour of applications to reduce on-die termination and switching energy by reducing the number of 1's transmitted over the channels. ZAC-DEST also provides a number of knobs for trading off application's accuracy for energy savings, and vice versa, and can be applied to both training and inference. We apply ZAC-DEST to five machine learning applications. On average, across all applications and configurations, we observed a reduction of 40% in termination energy and 37% in switching energy as compared to the state of the art data encoding technique BD-Coder with an average output quality loss of 10%. We show that if both training and testing are done assuming the presence of ZAC-DEST, the output quality of the applications can be improved upto 9× as compared to when ZAC-DEST is only applied during testing leading to energy savings during training and inference with increased output quality.