Lovely Course. Thanks Google

Focuses mostly on DNN

Labs have problems

Interesting!!

very helpful

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noice

Great

The content of this course might be a bit too much for one week compared to previous courses in the specialization. Also, it would be great if some of the labs are more clarified and introduce more opportunities for students to participate in writing code for the lab session rather than just going through it and running existing code. I did experience some issues installing the tf transform package for the last lab, which might not be a common issue, but was kind of frustrating as it prevents me from more exploration of the learned skills. Thanks for providing the course anyway. I learned a lot from it.

The subject is very interesting and I was alwyas curious about how Feature Engineering should be done with Tensorflow. I come from Pandas, where feature engineering is not that difficult, but with Tensorflow it is different and not that intuitive. Here in the course three different ways are presented. I guess I'll have to study more Apache Beam.

The concepts were taught well. However, a lot of code and cloud interaction was involved, making the labs a key piece of the material. Two of the labs didn't work because the Google lectures aren't up-to-date with the Google APIs. Although Coursera response to the bad labs was prompt, the Google team did not respond.

there was no big query lessons before , i only understood the consepts and nothing practical i have to repeat the whole course after studying bigquery, but in general feature engineering is very important for any ml model and everyone should take the lesson

maybe this course is very good, but for me I really hard to digest knowledge from this course. It needs a lot of time to understand the theory. Maybe it will be good if the course is given in more videos and slower pace. Thank you

It started very well, but in the end it got a little messy, difficult to follow, lost fluency of the concepts being explained and complex to understand the different technologies and how they compare and how should be applied.

More hands on activities is the common theme on all classes, its a lot of talking and not a lot of putting things together, follow the University of Michigan Python curriculum, that one is great for hands on learning.

Many installations on the pylab notebooks are broken. There are version conflicts all the time. Even if I run the notebook with no changes of my own, several errors appear. This wastes a lot of time.

some issues on some labs due to OAuth compatibility ...

see

ERROR: witwidget 1.5.1 has requirement oauth2client>=4.1.3, but you'll have oauth2client 3.0.0 which is incompatible.

Please update instructions. UI has been changed.

Some code doesn't execute. Last lab. Should be updated. This can be just one sentence (simply, versions of packages don't fit).

This wasn't a bad course, but it is more geared towards showcasing GCP features (BigQuery, Dataflow, Apache Beam, etc.) rather than teaching feature engineering.

The course focuses much more on the gcp tools rather than the feature engineering, labs were not easy to follow, some pieces of code did not work properly.

A good course overall. However, the last two labs didn't run since packages couldn't be installed. Please update these labs. :)

Some lab notebooks need to be updated. Especially for week 4, some setup steps are missing. Otherwise, good content.

Some of the Labs do not work and the information provided are not enough to debug the issue.

Too long for one week. I would suggest to split it in two or even three weeks

The work needed was waaaaay below a one week