Recommendation Systems on Google Cloud
Recommendation Systems on Google Cloud
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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
Course Info
Objectives
- Devise a content-based recommendation engine.
- Implement a collaborative filtering recommendation engine.
- Build a hybrid recommendation engine with user and content embeddings.
- Use reinforcement learning techniques for contextual bandits in the context of recommendations.
Prerequisites
• Prior familiarity with foundational machine learning concepts as covered in Machine Learning on Google Cloud.
• Familiarity with cloud concepts and fundamentals, networking, security.
• Basic proficiency with a scripting language such as Python as covered in the Google Python Crash course.
• Basic proficiency with SQL.
• Familiarity with Python and TensorFlow.
Audience
Aspiring machine learning data analysts, data scientists, data engineers, and programmers interested in learning how to apply machine learning to recommendation systems in practice.
Available languages
English
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