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Recommendation Systems with TensorFlow on GCP, Google Cloud

93 ratings
13 reviews

About this Course

***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.*** In this course, you'll apply your knowledge of classification models and embeddings 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 embeddings SPECIALIZATION COMPLETION CHALLENGE As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'Advanced Machine Learning with TensorFlow on GCP’ specialization. Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $150 value given there are 5 courses in the specialization). You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles. Your challenge awaits – begin learning on Coursera today! >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<...

Top reviews


Apr 01, 2019

awesomw complexity. some videos are very long, but worth revisiting


Jan 04, 2019

very good course. Complex sometimes but well worth my time

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13 Reviews

By Hicham AMINE

Apr 12, 2019

Excelent End to end recomandation systems course

By Facundo Ferrero

Apr 01, 2019

awesomw complexity. some videos are very long, but worth revisiting

By Jesper Olsen

Mar 14, 2019

The labs by themselves - 'jupyter' notebooks - are good, but they were obviously developed in some other context and then reused in coursera. This is a problem. There about 6 labs per course - in each of the 10 courses of the two Machine Learning specialisations. Each lab starts the same way - connect to the google cloud, allocate a vm, check out a git repository - exact same repository for all labs. It takes 10 minutes. Not 10 minutes where you can go away and have a cup of coffee - 10 minutes where you have to be there and accept terms, answer 'Y' etc. If the labs are done outside the Coursera context you would be able to pick up where you left off in the previous lab - zero setup time. But not here - it is too much wasted time: 10*6*10=600 minutes. Evil.

By Carlos Viejo

Feb 17, 2019

Excellent Course, in particular, the explanations around Google's Cloud Composer, the quality of the templates and the labs, thanks very much Lack and all your team for putting together this great specialization and course.

By Sanjay K

Jan 12, 2019

No tensorflow.. lot of talk not a single math.. NOt good

By Sinan Gabel

Jan 07, 2019

Great work by Google, a lot of material and system walk-throughs. Apache Airflow / Google Composer is a smart tool but perhaps too complicated where more simple e.g. bash cron scripts could suffice - however it is understood that for truly scalable end-to-end systems the traditional single-cloud-virtual-machine solutions will not do. We are shown how that could look like and much more.

By Luiz Gustavo Martins

Jan 04, 2019

very good course. Complex sometimes but well worth my time

By Hemant Devidas Kshirsagar

Dec 03, 2018

A very challenging course.

By Harold Lawrence Marzan Mercado

Nov 29, 2018

This was a large and hard course on ML and in particular for Recommendation Systems. The videos were way to long. The content was very interesting. I've learned new algorithms like WALS for Collaborative Filtering and others more.

The Cloud Composer technology is cool for Keeping your System learning all the time.

Thank you Googlers.

By Afreen Ferdoash

Nov 28, 2018

Theoretical part was great and some of it was really new for me(eg. WALS, contextual recommendations).

Lab was really pointless as the time provided did not justify the problem difficulty level. But, I guess that was necessary as resources used were expensive. Another pain point was creation of an account every time.