From the large amount of educational material that is accessible on the Internet, and especially with MOOCs, university students (specifically the case of the Professional School of Systems Engineering of the National University of San Agustín of Arequipa) see complicated the task of choosing some of the online courses that the e-learning websites offer. Students must access each platform, search for available courses according to filters that the student sees fit, read the course contents in detail, verify the instructor's experience, duration of the course, its methodology, and other relevant characteristics for the students. Mainly, it is tedious to navigate between hundreds of courses from different platforms and find a course that fits their interests (usually not very well defined by their lack of experience or knowledge in the field of computer science). Under these circumstances, this research work seeks to develop a Recommender System based on the content of all available courses that are being offered on the edX and Udemy platforms, using Web Crawling and Web Scraping techniques to obtain the information. Thus, the system recommends to the student what courses they could study from the edX and Udemy platforms, which fit their interests in accordance with subjects they have studied at the university as part of the curriculum plan. The recommendation given will be based on the similarity between the contents of each e-learning course and contents of university subjects that the student has identified as of greater interest. To reach the objective, data has been analyzed on the courses of the two mentioned e-learning platforms and the content of the 71 subjects that make up the syllabus of the Systems Engineering career. The proposed system achieved its objective of providing objective recommendations to students during the decision making process in which e-learning courses should be enrolled according to their interests.