Understanding Algorithms for Recommendation Systems
Understanding Algorithms for Recommendation Systems
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours 13M | 253 MB
Genre: eLearning | Language: English
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours 13M | 253 MB
Genre: eLearning | Language: English
Recommendations help monetize user behavior data that businesses capture. This course is all about identifying user-product relationships from data using different recommendation algorithms.
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.
Understanding Algorithms for Recommendation Systems
