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Understanding Algorithms for Recommendation Systems

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.
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Length 2.2 hours
Starts On Demand (Start anytime)
Cost $29/month (Access to entire library- free trial available)
From Pluralsight
Instructor Swetha Kolalapudi
Download Videos On Windows, MacOS, iOS, and Android Pluralsight app
Language English
Subjects Data Science Programming IT & Networking
Tags Data Science Algorithms Machine Learning Data Professional

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Rating Not enough ratings
Length 2.2 hours
Starts On Demand (Start anytime)
Cost $29/month (Access to entire library- free trial available)
From Pluralsight
Instructor Swetha Kolalapudi
Download Videos On Windows, MacOS, iOS, and Android Pluralsight app
Language English
Subjects Data Science Programming IT & Networking
Tags Data Science Algorithms Machine Learning Data Professional

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