Practical Recommender Systems For Business Applications in R
Implement content-based, collaborative and hybrid recommendation approaches in R and relate user-item preference predictions to e-commerce personalization
Implement content-based, collaborative and hybrid recommendation approaches in R and relate user-item preference predictions to e-commerce personalization
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Recorded Udemy course advertised with a public course preview. Exact lesson count may be unknown. Purchase or organization-subscription terms and current availability require checking
An original example for comparing learning plans. Ask whether the course teaches this task, includes practice and offers feedback; the diagram does not describe a provider’s course.
Sundog Education by Frank Kane , Frank Kane , Sundog Education Team
Build and evaluate content/collaborative, matrix-factorization, neural and session-based recommenders, with Netflix/YouTube examples and production-scale methods. Technical recommendation training transfers to customer content/product…
14 sections • 130 lectures • 11h 47m total lengthCheck current priceEnglish
Shared topic: Customer analytics and personalization
Lazy Programmer Inc. , Lazy Programmer Team
Implement collaborative filtering, matrix factorization, neural models and ranking using NumPy/Keras/TensorFlow/Spark, broad technical recommender course is adjacent to personalization marketing
15 sections • 94 lectures • 12h 49m total lengthCheck current priceEnglish
Shared topic: Customer analytics and personalization
University of Colorado System
Marketing applications of R-based CART and random forests, cross-validation/model comparison, causal trees and forests for targeting, segmentation, principal-component analysis and recommender systems
Four modules, 29 assignments. Intermediate. Coursera estimates two weeks at ten hours per weekCheck current priceEnglish