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.
I enjoyed this course too much, usually every company wants a recommended system, but the courses or examples available on the web are few. Very well explained many theoretical aspects.
I would like that in the course provide for more readings to keep it.
It is a wonderful course, to learn about the practical implementation of recommendation systems on Google Cloud Platform.
An awesome course. Excellent explanation of concepts as well as programs.Easy lab setups and hands on learning.
Composer component takes too long to initiate. Almost more than 25 minutes. Please fix it.
very good course. Complex sometimes but well worth my time
Succinct course on building recommendation systems!!
you should move into tensorflow 2 instead of tf 1.x
Again, very information and super fun. Thank you.
Some of the labs had no work for me to do whatsoever - just run the notebook as is. Other than that, great course. Cheers!
This course and specialization are a great way to learn how to use the Google Cloud Platform with Tensorflow to build cutting edge systems.
Amongst all tensorflow courses this is probably the most useful. Using AI to make better and automated recommendations can benefit most businesses.
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The courses is not up to date. They are using TF1 instead of TF2 and sometime python2. The WALS is not supported in TF2 and it is the central algorithm for collaborative filtering. I only learned that I should NOT build a recommendation system using TF,
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.
One star, but not to content. But because the course don't have "Audit" option. It's mean that after subscription ended and you received certificate, You can't more access to video material in course. When subscription active, You can use mobile application and download video material for studying offline. Before yours subscription ened, copy video material to safe place for later review.
p.s.
But the course content deserves a higher mark - 4-5 stars.
Not very intuitive explanation compared with previous four courses.
Overall a good and comprehensive introduction to recommendation systems.
On the downside, some functions used were deprecated, there was sometimes inconsistency between versions in labs (for example automatic upgrading to Tensorflow 2.0, which was incompatible with other libraries used in the lab and things like that).
Also, in my opinion an insight into the models' results is lacking. There was a nice explanation of the performance in content-based part, but later during hybrid and context-aware systems there was no comment on models' accuracy in comparison to the original basic solution.
you should move into tensorflow 2 instead of tf 1.x
The lectures excellent but the exercises were tedious and boring. Code only to be understand by the creator only.
No tensorflow.. lot of talk not a single math.. NOt good
The course lectures were decent, but the labs are full of bugs and erroneous or incomplete instructions. The final lab of the course (and also the specialization) has been unavailable for a few days now and tech support has not been helpful.
The course is not updated. The labs are completely different from the titles and the lab solution videos. Quite disappointed.
I love math, but unnecessary complexity was added to the content, making the course unpleasant to follow.
Most of the coding exercises use TF 1.X which is rather dated. I was hoping it would use TF 2.X instead. Also the coding exercises do not leave anything for the students to do. The video instructions were clear and well done.
This one was the hardest of the specialization. A schema accompanying the code explanation would have been useful.
Bad course overall. It has some theoretical content in it, but concepts are not explained in depth and videos are sometimes hard to follow. Speaking of assignments, I had no motivation for completing them, because, firstly, they are not graded, and secondly, they are terribly designed and one won't get much from them.
Very poor course. Assignments are very weak and they do not test anything - there is no grader, you can just verify solution by watching the lab videos.
The content is OK, but web is full of good content on recommendation systems.
If you want to take this course by any means do not pay for it - by paying you only get access to qwiklab platform which sucks for these kind of assignments, and anyway you can do almost everything from the course on GCP free tier, and also not lose your progress every time you log out of lab.
Some labs with bigquery and the movieLens are not working - including the solutions, which is really time consuming and frustrating.
Labs illustrate very well the concepts and clarify the practical issues and solutions with gcp & tf. Excellent teaching !
Before the DAG part everything was quite understandable and useful. I am completely lost with the DAG and cloud composer part. Maybe it is not a very good idea to teach some tool that is still under developing.
For this course, some part could be break into small chunk, and explain more detail. Generaly, this course is awesome!
确实教了东西，不过可用性很差，tutorial基本上对实践没什么帮助
some labs are deprecated.