Abstract
Online education will be paired by developing intelligent systems that can provide personalization to the learner. To that extent ,this paper is proposing the design for an Online Personalized Learning Remediation/Tutoring Tool to help learners find the most suitable teachers for certain topics. The tool relies on a dataset of teacher profiles, comprising the subjects taught, video links, ratings, and experience, so that using machine learning techniques, it could recommend top educators. Applying feature extraction using CountVectorizer and recommendation based on cosine similarity, it will ensure the robust and personalized search experience to the learners. The tool will augment the process of learning by not wasting time of the learner searching for an appropriate teacher and provides an uninterrupted user experience through an easy-to-understand web interface. This system increases the satisfaction of learners along with optimizing their ability to engage with the relevant teachers effectively.