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Recommendation Systems on Google Cloud

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Recommendation Systems on Google Cloud

magic_button Machine Learning Pipeline Machine Learning Machine Learning Models
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8 hours Intermediate universal_currency_alt 25 Credits

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.

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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
What do I do when I finish this course?
After finishing this course, you can explore additional content in your learning path or browse the catalog.
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Interested in taking this course with one of our authorized on-demand partners?
Explore Google Cloud content on Coursera and Pluralsight.
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View the public classroom schedule here.
Can I take this course for free?
When you enroll into most courses, you will be able to consume course materials like videos and documents for free. If a course consists of labs, you will need to purchase an individual subscription or credits to be able consume the labs. Labs can also be unlocked by any campaigns you participate in. All required activities in a course must be completed to be awarded the completion badge.
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