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We present a novel approach to machine learning, called ABML (argumentation based ML). This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert's arguments, or reasons, for some of the learning examples. We require that the theory induced from the examples explains the examples in terms of the given reasons. Thus arguments constrain the combinatorial search among possible hypotheses, and also direct the search towards hypotheses that are more comprehensible in the light of expert's background knowledge. In this paper we realize the idea of ABML as rule learning. We implement ABCN2, an argument-based extension of the CN2 rule learning algorithm, conduct experiments and analyze its performance in comparison with the original CN2 algorithm.