In this Machine Learning online course, we learn what recommendation systems are, their applications, critical approaches to building them – Popularity based systems, Collaborative filtering, Singular Value Decomposition, etc.

Introduction to Recommendation systems

As the name suggests, recommendation systems help predict some products’ future preference and recommend the best-suited items to the user. In this module, you will learn how to use these systems to help people choose the best products.

Content based recommendation system

First, we collect the data from the user explicitly or implicitly. Then, we create a user profile based on this data that is later used to suggest to the user. The user provides us with more information or takes more actions based on the recommendation, enhancing the system’s accuracy. This technique is known as Content-based Recommendation System.

Popularity based model

Popularity based model is a type of recommendation system that works based on popularity or anything that is currently trending.

Collaborative filtering (User similarity & Item similarity)

Collaborative Filtering is a joint usage of algorithms where there are several ways to identify similar users or items to suggest the best recommendations.

Hybrid models

A Hybrid Model is a combination of multiple classification models and clustering techniques. You will learn how to use a hybrid model in this module.