Implement collaborative filtering, matrix factorization, neural models and ranking using NumPy/Keras/TensorFlow/Spark, broad technical recommender course is adjacent to personalization marketing
Lazy Programmer Inc. , Lazy Programmer Team · Udemy
Implement collaborative filtering, matrix factorization, neural models and ranking using NumPy/Keras/TensorFlow/Spark. Broad technical recommender course is adjacent to personalization marketing
Audience and starting point
Selected published requirements: “For advanced sections, know calculus, linear algebra, and probability for a deeper understanding”. See the provider page for the full requirements and current tool terms
Practice and assessment
Practice: A collaborative-filtering exercise uses the MovieLens 20M data to build a similarity-based predictor and compare training and test error. A matrix-factorization exercise prompt is also listed
Access and subscriptions
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.
Shared topic: Customer analytics and personalization
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
Build a positive/negative review classifier in a ten-lesson/79-minute project after Python/NumPy/pandas/ML/TensorFlow foundations, the whole course counts once and is adjacent technical training
7 sections • 105 lectures • 16h 37m total lengthCheck current priceEnglish
Shared topic: Customer analytics and personalization
Minerva Singh
Implement content-based, collaborative and hybrid recommendation approaches in R and relate user-item preference predictions to e-commerce personalization
5 sections • 36 lectures • 3h 19m total lengthCheck current priceAsk about teaching language