• Vedula Lakshmi Spandana
• Joga Rohit Abhinav
• Pothu Naga Sampath
• Mohammed Musaiyab Ahamed

Recommender systems compile a list of all currently accessible content, filter it in compliance with content moderation standards, and then narrow the list down to the items that users are most likely to be interested in. The objective is to create a system for recommending courses that takes user needs into account. A recommender system is helpful because it only shows consumers the courses they want from a huge selection of online courses. In this project, streamlit is used to create a course recommendation system that uses the course name and desired amount of suggestions as inputs to suggest all relevant courses on Udemy. Cosine similarity and content filtering are used to accomplish this. Although there are other strategies, such as clustering models and matrix factorization, Cosine similarity guarantees superior recommendation accuracy. Cosine Similarity calculates how similar two vectors are to one another. It goes from -1 to +1, with +1 denoting similar items and -1 denoting differences between the compared items. A csv file with 3672 rows is used to train the machine learning model. The streamlit created web page is then attached to this trained model. The result would be a list of relevant courses including information like the course's title, url, rating, and other details that are desired.

ML (Machine Learning), Content Based Filtering and Cosine Similarity.

"COURSE RECOMMENDATION SYSTEM USING CONTENT BASED FILTERING AND COSINE SIMILARITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.i210-i218, March-2023, Available :http://www.jetir.org/papers/JETIR2303833.pdf

"COURSE RECOMMENDATION SYSTEM USING CONTENT BASED FILTERING AND COSINE SIMILARITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppi210-i218, March-2023, Available at : http://www.jetir.org/papers/JETIR2303833.pdf