With the increase in Technology Enhanced Learning (TEL), the effective retrieval and availability of Learning Objects (LOs) for course designers is a significant concern. Text-based LOs can be accessed from structured LO repositories (LORs) and unstructured sources. Different LOR structures and semantically diversified LOs hinder the process of course designing because of various repository formats and standards. The proposed research provides an innovative Machine Learning and filter based context-aware LO recommender System for the designing of course. The proposed model helps to easily search and access diversified and semantically related LOs. The proposed solution will also help the academia to design customized courses gratifying higher education curriculum needs while reusing existing LOs. Through automatic feature extraction and classification, the proposed system -- Dynamic Recommendation of Filtered LOs (DRFLO) -- retrieves a set of semantically relevant LOs from heterogeneous LORs. Using the additional recommendation and ranking models, LOs are contextually ranked. Finally, a ranked list of LOs is recommended on a simple query from the course designer according to learning preferences while designing a course. The performance of the proposed model is quite remarkable in terms of precision, recall and F-measure. Furthermore, the experimental results have also been compared with a common search engine and an accuracy of 93% was achieved. The proposed research further produced, pre-test and post-test experiments to ensure the validity of the DRFLO approach. The proposed DRFLO System is a machine learning based research effort which provides an efficient mean for retrieving LOs while designing a course.