Many robotic tasks require fast and accurate force sensing capabilities to ensure adaptive behavior execution. While dedicated force-torque (FT) sensors are a common option, such devices induce extra costs, need additional power supply, and add weight to otherwise light-weight robotic systems. This paper presents a machine learning approach for estimating external forces acting on a robot based on common internal sensors only. In the training phase, a behavior-specific proprioceptive model is learned as compact representation of the expected proprioceptive feedback during task execution. First, the proprioceptive sensors relevant for the given behavior are identified using information-theoretic measures. Then, the proprioceptive model is learned using deep learning techniques. During behavior execution, the proprioceptive model is applied to actual sensor readings for estimation of external forces. Experiments performed with the UR5 robot demonstrate the ability for fast and accurate force estimation even in situations where a dedicated commercial FT sensor is not applicable.