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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

Minerva Singh · Udemy

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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

Learning decision: Start with a content brief; make Two message drafts; check Claims, brand voice and audience fit.
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Building Recommender Systems with Machine Learning and AI

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
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Related skills

Recommender Systems and Deep Learning in Python

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
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Marketing focus

Machine Learning for Marketers

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
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