Feature Engineering

great course and very intrsting

this is useless...google is advertising their product and making us pay for it. They should learn dr Andrew Ng and create courses which teach us without using a specific platform.

Labs vms are to slow. Speaker is difficult to understand. Mic varies and speech pattern is not clear. The presentations need some graphics rather than a guy talking. Sketch out the ideas on a white board rather than talking 5 minutes to a single slide.

Ultimately I found this course to be disappointing, because the Google APIs for DataFlow, BigQuery, etc. are unusable with the provided QuickLabs account. When you try to activate any API during the labs, it asks you for a location. It is a required field that says: "You must select a parent organization or folder." Clicking this option reveals a single organization called "no organization," which is not a legitimate choice. APIs cannot be activated and then cannot be used in the lab.Because of this I was unable to actually do many of the labs that required the use of the Google APIs including the keystone lab "Improve ML model with Feature Engineering" where the taxi-fare prediction model is refined into a perfected state.I'm upset that I paid money for this.

Not very clear + lack of real student practice

Pros: Tensorflow is an excellent framework for deep learningCons :1- The way this material is designed is 10 X SHIT2- Either teach properly or don't teach at all.

Basically this course would receive four stars, but repeated problems with qwiklabs had a severe impact on my overall experience. I got thrown out three times in a row (and my account locked) during dataflow lab. Every time I had to request unlockin of my account, which took half a day every time. When requesting advice to avoid this error, I got offered the general and vague explanation that I "should only use the resources required by the lab". I am 100% sure that I didn't use any extra resources, including zones and regions. The Coursera's helpdesk went behind the excuse that Qwiklabs is a third-party service. That may be the case, but since Qwiklabs has been integrated into the Courseras' course, the ultimate responsibility lies with Coursera.I hope that Coursera will co-operate with Qwiklabs to sort out this very annoying problem.

the lectures are good, can be boring. The course would have been more interesting if it had thought-out assignments instead of demo-code to just run as labs

Great instructor but (1) the coding challenges are buggy and don't really teach you anything and (2) a lot of the material in this course is tedious for someone with professional training in AI but no experience with GCP

The gap between the lecture and the coding is too big. The coding sessions need to be more interactive to be useful.

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 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.