This course delves into the strategies used by Kaggle Grandmasters of NVIDIA to excel in a data science competition focused on building a recommendation system for e-commerce. The learning outcomes include understanding the 2-stage model of recommender systems, creating candidate generation and co-visitation matrices, feature selection and engineering for a reranker model, and model ensembling. The teaching method involves video lectures with detailed explanations and real-world examples. This course is intended for data scientists, machine learning engineers, and anyone interested in mastering recommender systems and participating in data science competitions.
Overview
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
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Reviews
4.5 rating, based on 2 Class Central reviews
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Abiyyu DaffaI 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. -
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
