Recommender systems produce a compilation of all available content, analyze it in accordance with moderation of content criteria, and then reduce the list to items that users seem most inclined to be interested in. The goal is to develop a course recommendation system that considers the demands of the users. The recommendation system is useful, since it only displays the courses that customers want from a large range of online courses. In this study, we use unsupervised learning approaches such as similarity measures, K-means, and Principal Component Analysis (PCA). It is a content-based recommendation system which will group users with similar interests together and suggest the most popular courses to the user based on their user profiles. The dataset used for training the model has courses covering the most engaged genres like Machine learning, Data Science, Big Data, and so on.