AI Marketing Courses

Related skills · check the marketing fit

Building Recommender Systems with Machine Learning and AI

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…

Sundog Education by Frank Kane , Frank Kane , Sundog Education Team · Udemy

Inside the course

What you’ll cover

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 discovery

Audience and starting point

Selected published requirements: “Some experience with a programming or scripting language (preferably Python)”. “Some computer science background, and an ability to understand new algorithms”. See the provider page for the full requirements and current tool terms

Practice and assessment

Practice: The outline lists recommender-system quizzes and a programming exercise involving Boolean values and loops. One four-question check includes discussion of the answers

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

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

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Related tasks, tools and formats. Check the differences before choosing.

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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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Supervised/Unsupervised Machine Learning Projects

Aaron Sanchez

Implement customer-churn classification, behavioral customer clusters and a product recommender using collaborative algorithms within a four-project ML compilation, no component is counted separately

3 sections • 28 lectures • 8h 58m total lengthCheck current priceEnglish
Read course details