Today, there is a vast amount of online material for learners. However, due to the lack of prerequisite information needed to master them, a lot of time is spent in identifying the right learning content for mastering these concepts. A system that captures underlying prerequisites needed for learning different concepts can help improve the quality of learning and can save time for the learners as well. In this work, we propose an unsupervised approach, UPreG, for automatically inferring prerequisite relationships between different concepts using NLP techniques. Our approach involves extracting the concepts from unstructured texts in MOOC (Massively Open Online Courses) course descriptions, measuring semantic relatedness between the concepts and statistically inferring the prerequisite relationships between related concepts. We conducted both qualitative and quantitative studies to validate the effectiveness of our proposed approach. As there are no ground truth labels for these prerequisite relations, we conducted a user study for the evaluation of the prerequisite relations. We build the concept graph using prerequisite relations. We demonstrate few examples of the learning maps generated from the graph. The learning maps provide prerequisite information and learning paths for different concepts. [For the full proceedings, see ED615472.]