Hated this course. Too many bugs! I had to settle for a 75% on Lab 7 due to unchangeable code errors (ie deprecated methods/functions)

Achei este curso LONGO de mais, para uma sprint de uma semana apenas, deveria ser 2 semanas. por isso não dou 5 star.

Okay overview, not too in depth and doesn't explain all of the code very well

The course was good for the data engineers beginning with GCP, a bit more syntactical information can be added in the notebooks, it will this course even better.

Excellent course. Though a bit slow for those who have any good knowledge on ML already

Lab 4 throwing error after rectification also lab status is 50% completion status showing. Overall good.

if you read this before taking this course, believe me you will need PATIENCE!

LAB 7 NOT WORKING PROPERLY

El RSME inicialmente se fijo en $8 dolares.

Al final utilizando el metodo con Ingenieria quedo menor a $4.

Me quedo la incognita de como es posible que con los metodos intermedios pueden ser tan inexactos casi un 50% de error con respecto al valor esperado? En la vida real eso se traduce a millones de dolares en perdidas utilizando herramientas de Google Cloud Platform.

The course is not very well balanced in my opinion. It is very sparse in very beginning and labs do not differ much. Last two modules are too much information and labs complexity grows exponentially.

too specific

Much harder than the three first courses. Specially for someone who didn't know tensorflow at all!

Make assignments on big data more interactive, and give more time to complete.

Need good understanding of Python and TensorFlow syntax.

Good introductory course for use of TF on GCP! Prior knowledge of TF would be handy as well. Video recordings needs improvement. Video clips from a group class setting appear in the course as well.

Course is fine, but attention to detail is horrible. I mean, 4 sec videos... Old material which is not removed. Duplicated videos...

Nice overview, but not much depth.

The theory of ML the course introduced is quite basic, the usage of TF isn't taught in detail. So I am a little disappointed, besides that the accent of the teacher is a little bit hard to understand.

Quality of material is mediocre:

-tons of useless short videos

-inconsistent sound level

-missing lab

It would be better to present a easier material on TF. This is not for someone starting on TF.

Course content is excellent. Organization is not good. It seems like there were some errors in the order of a few quizzes as well as links to the wrong sections. If you take this course and find yourself lost on a quiz or a lab; skip it watch more content, then go back. It was probably out of order.

The course appeared to be put together poorly by pulling from many different sources. Therefore it lacks coherence. There are many videos that are under 1 minute long and just there to make a statement or two or just to repeat the last video. Some videos end before instructor could complete his statement. Some videos are recordings from a classroom setting and makes it hard to follow. Also would've appreciated some support from the course team for questions posted on the discussion forum. I think topics such as machine learning in GCP deserves a bit more respect. This course needs to be planned from scratch with MOOC and Coursera format in mind.

The title doesn't reflect the real content of this course, it is more like an introduction to start developing on ML solutions rather than to actually getting in to production on ML google engine

Great course in many ways, but I encountered a lot of errors/warnings regarding outdated TF-versions. Kind of felt unprofessional at times because of that. Otherwise - great content, great tutorials, great lectures and great labs!

Need to dig in more to understand