Recommendation Systems with TensorFlow on GCP

Course Modality

Instructor-led (classroom)

Course Level


Course Time


Course Language


Course Overview

In this course, you’ll apply your knowledge of classification models and embedding to build a ML
pipeline that functions as a recommendation engine.

  • Devise a content-based recommendation engine
  • Implement a collaborative filtering
    recommendation engine
  • Build a hybrid recommendation engine with user and content

Why The DataTech Labs ?

Self-Paced Online Video

A 360-degree learning approach that you can adapt to your learning style

A 360-degree learning approach that you can adapt to your learning style

Engage and learn more with these live and highly-interactive classes alongside your peers

24/7 Teaching Assistance

24/7 Teaching Assistance Keep engaged with integrated teaching

Online Practice Labs

Projects provide you with sample work to show prospective employers.

Applied Projects

Real-world projects relevant to what you’re learning throughout the program

Learner Social Forums

A support team focused on helping you succeed alongside a peer community

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Course Curriculum

In this module, we review the scope and plan for the course, define what recommendation
systems are, review the different types of recommendation systems and discuss common
problems that arise when developing recommendation systems.

Content-Based Recommendation Systems

In this module, we demonstrate how to build a recommendation system using
characteristics of the users and items.


In this module, we show how the data of the interactions between users and items from
many different users can be combined to improve the quality of predictions.

Neural Networks for Recommendation Systems

In this module we show how various recommendation systems can be combined as part of
a hybrid approach.

Building an End-to-End Recommendation System

In this module we put all the pieces together to build a smart end-to-end workflow for
your newly built WALS recommendation model for news articles.


In this final module, we review what you have learnt so far about recommendation
systems and the specialization more broadly.

Recommended Exams

Machine Learning Engineer

Professional Machine Learning EngineerBETA

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.


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