Overview
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Explore strategies employed by Kaggle Grandmasters of NVIDIA to secure top positions in a data science competition focused on building a high-functioning recommendation system for e-commerce. Learn about the two-stage model approach, including candidate generation using co-visitation matrices and reranker model development with feature selection and engineering. Discover insights from second and third-place solutions, model ensembling techniques, and participate in a Q&A session. Gain valuable knowledge about recommender systems, data science competitions, and advanced techniques used by industry experts in this 47-minute video from the Grandmaster Series.
Syllabus
– Introduction
– Overview & Summary of the Challenge
– Recommender Systems - 2 Stage model
– Stage 1: Candidate Generation & Co-visitation matrices
– Co-Visitation matrices explained
– Stage 2: Reranker model - Feature selection & engineering
– Second-place solution
– Third-place solution
– Model Ensembling
– Q&A Session
Taught by
NVIDIA Developer
Tags
Reviews
4.6 rating, based on 5 Class Central reviews
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"Mastering Recommender Systems provided an in-depth exploration of recommendation algorithms and their applications. The course covered collaborative filtering, content-based methods, and hybrid approaches, all supported by practical coding exercises. This experience has significantly bolstered my ability to design and implement effective recommender systems, enhancing my technical skills and understanding of user personalization strategies."
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I like how the way you guys explain it, it is simple and understandable for people who wants to learn about how recommendation in our online shopping works.
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Course is very good. Some improvement are required to make this more effective.But in the end i want to say that course is very good
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its good, i like this materi. the materi good, this can deliver good. from sesion 1 to sesion 8 good and easy to understand
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It was a good course and It helped me a lot in improving my skills and Education For my future Goals