Dive into the world of recommendation systems through a comprehensive series of tutorials covering various techniques and implementations. Learn to build a movie recommender system using Python's weighted hybrid technique, explore creating recommendation systems with nearest neighbors, and master book recommendation systems using Pearson correlation and collaborative filtering. Discover content-based recommendation systems, delve into machine learning applications for recommendations, and understand the concepts of cosine similarity and cosine distance. Gain practical skills to develop sophisticated recommendation algorithms for diverse applications.
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
