Understanding Algorithms for Recommendation Systems

Recommendations help monetize user behavior data that businesses capture. This course is all about identifying user-product relationships from data using different recommendation algorithms.
Course info
Rating
(66)
Level
Beginner
Updated
Feb 1, 2017
Duration
2h 13m
Table of contents
Description
Course info
Rating
(66)
Level
Beginner
Updated
Feb 1, 2017
Duration
2h 13m
Description

In addition to monetizing user behavior data, recommendation algorithms also help extract actionable recommendations from raw user ratings/purchases data. This course, Understanding Algorithms for Recommendation Systems, will cover the different types of Recommendation algorithms - Content-Based Filtering, Collaborative Filtering, and Association Rules Learning and when to use each of these types. You'll also learn about the specific algorithms such as the Nearest Neighbors model, Latent Factor Analysis and the Apriori Algorithm and implement them on real data sets. Finally, you'll learn about mining for rules that relate different products. By the end of this course, you'll be able to choose the recommendation algorithm that fits your problem and dataset, and apply it to find relevant recommendations.

About the author
About the author

Swetha loves playing with data and crunching numbers to get cool insights. She is an alumnus of top schools like IIT Madras and IIM Ahmedabad.

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