This repository contains machine learning models for an online course recommender system, utilizing the Coursera dataset.

The purpose of this project is to provide a personalized course recommendation system based on the user's preferences and the Coursera dataset. The implemented models aim to enhance the learning experience by suggesting relevant courses tailored to individual users.

The collaborative filtering model is implemented to recommend courses based on the behavior and preferences of similar users. It leverages user-item interactions to make personalized recommendations.

The content-based filtering model recommends courses by analyzing the content of the courses and matching them with user preferences. This model is designed to suggest courses that align with the user's interests.

The KNN model is utilized to recommend courses by identifying patterns and similarities between users and courses in the dataset.

K-Means clustering is applied to group courses based on certain features, providing a more nuanced understanding of course relationships.

An ensemble model combining Logistic Regression and Random Forest techniques to improve the accuracy and robustness of course recommendations.

SVM is employed to classify and recommend courses, taking into account the characteristics of both users and courses.

NLP techniques are applied to analyze and understand textual information associated with courses, contributing to more context-aware recommendations.