This course covers the fundamentals of recommendation systems, including weighted hybrid techniques, nearest neighbors, Pearson correlation, collaborative filtering, content-based recommendations, and machine learning. Students will learn how to implement recommendation systems using Python and understand concepts such as cosine similarity and cosine distance. The course is designed for individuals interested in learning about recommendation systems and their applications in various domains.
Overview
Syllabus
Tutorial 1- Weighted hybrid technique for Recommender system.
Movie Recommender System using Python.
Tutorial 2- Creating Recommendation Systems using Nearest Neighbors.
Tutorial 3- Book Recommendation System using Pearson Correlation.
Tutorial 4- Book Recommendation using Collaborative Filtering.
Tutorial 5- Content Based Recommendation System.
Recommendation Systems using Machine Learning.
Cosine Similarity and Cosine Distance.
Taught by
Krish Naik
