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Good Course! The instructors are great and explain the concepts well. The new course 1 is definitely better than the older version.

However there is a long way to go to make these courses such that even a newbie can go thru them without a programming background. Please add courses that provide optional training to cover the background needed in e.g. SQL or Networking or Python programming. It would be very helpful if the instructors didn't copy scripts/templates and walked thru them step by step or at least have optional video to explain what they are doing in these

You guys need to direct people to follow these courses in a sequence - e.g. first do Data Analyst courses, then GCP Architect courses 1 to 5 to get basic understanding of the platform and then do Data Engineering courses.

More labs and lab time are needed to help build confidence. Challenges and solving problems by ourselves without having to worry about time limit on lab time would definitely make students more use to the platform.

It is nice that Google staff prepared these tutorials and demos as a comprehensive introduction to the Google Cloud Platform. However, the materials feel very dense for a 2 week course, with more than 500 slides from the module resources to cover and a few hours of laboratory work. The time estimation does not include reading the provided links for each module to understand different GCP services. As a beginner to the platform, I find it easier to divide the 5 modules over several days, instead of watching 2 modules back to back on the same day. For perspective students, note that it takes a little more effort to replicate the demo activities because the GCP web UI has updated since the course videos were recorded, but you can also treat it as an opportunity to click around and see what the platform does. It would also be convenient if we can save a copy of the lab activities as PDF file or Word Doc.

El contenido es adecuado y por ser un curso de introducción es bastante completo. Lo único que ha chafado el curso ha sido tener que hacer los labs varias veces a fin de conseguir la nota.

No he terminado de resolver por qué he tenido tantos problemas, la cuestión es que en los foros hay mucha más gente que ha tenido problemas semejantes. Al final esto entorpece el aprendizaje, si llevas un buen ritmo tener que hacer las cosas 3 y 4 veces parece un poco exagerado.

De todos modos, me llevo una buena experiencia ya que los problemas finalmente se han resuelto. Tengo que añadir que los problemas mencionados anteriormente creo que son más problemas de Quiklabs. La nota ya no me subía en su plataforma (aunque hiciese paso por paso la práctica), y por tanto, esa nota es la que se reflejaba en Coursera.

This was a good overview of the services Google Cloud Platform offers. There were a few bugs with the quizzes, but overall it was put together well and had a lot of information for a short course. I think it is a good introductory-level course for a person who has some background in Data Science and cloud computing, but not much experience with Google Cloud Platform. The tutorials and exercises are quick walk-throughs that can be completed in 10-15 minutes each. They give you a feel for how the Google Cloud APIs work and what some of the capabilities are, but don't go into much depth. This is an ideal course for a person who is considering using Google Cloud Platform but isn't sure what the advantages and capabilities are.

Good contents. However, the scoring system is not working well, and I could never finish the course even though I got full mark at least 5 times in the machine learning application lab. Besides, after one week, I was charged 42 euros and feel so sad about this experience. I would like to mark 5 stars in the content, but the scoring system didn't work well with Firefox, and even had a trouble with Chrome, and I was charged 42 euros even though I finished all the contents within one week. So, sadly, I can only put one star as an overall evaluation of this course.

Edit: After some interactions with the support team, I was able to see this course completed. So, I put 4/5 stars.

Some times, there are differences in the way the instructors setup programmatic parameters vs. quiklabs. Also, scoring is best only if exactly follow up what the instructors say, for example folder names, though that is not necessary as long as consistency is maintained. This is important especially the student fails in an attempt and the system does not reset all the parameters, for example the system does not reset all the previous folder names, unnecessarily, and painfully in terms of time. All these makes it less robust for shorter knowledge based executions. Thanks for the course.

Resolutions: Invest your time in the script part to resolve the issues; they are great!

The instructor is very clear and conscise. He gives people time to digest all the information which helps us to understand every aspect of its speech. The exercises are good as well, even though I believe it could be a bit more explained in details each part of each exercise.

I just didn't give 5 starts because I thought there could be a more bigginer's training as I have zero experience with the Cloud and within developing with Python.

Because of that, I realized it is better to have some good knowledge on it before hand.

I'm very inclined in taking the next trainings though.

Thanks for such nice teaching.

Most of the course was well made, it explain very well the fundamental.

The only thing that didn't satisfy me were some labs, where not everything worked properly.

In the lab with datastudio I think there where too much things to and the time wasn't enough I think the time should be augmented for that lab.

And the last lab had problem with the pretrained model it always predicted one label.

Also I think it could be useful to make a deploy of a model on a endpoint, if the problem is the training time then the deploy could be done with a pretrained model.

There were a few frustrating technical issues caused by Google Cloud Platform outages, using Qwiklab was fairly clunky, the tutorials weren't as in-depth as they could have been (really, you're only marked on your ability to follow the basic instructions to create compute engine instances, cloud datastore buckets, etc.) and there were Coursera issues (e.g. quiz result page not showing what the original question text was for your answer). The lectures themselves were informative and utterly redeemed these issues.Thanks, Lak!

Good overview with hands on labs. Might not be as useful if you already have some experience with GCP. This course is about familiarisation with GCP products, it doesn't require you to understand the code that's used in the labs, however it gives you a good perspective about the capability. It certainly opened my mind about the potential solutions and usability of the platform. I still find the price for this course a bit high, it's not the best value for money given that this is also promoting Google products.

Very clear course covering basics of cloud computing and machine learning, as well as introducing Google Cloud Platform tools and services.

Lab sessions are easy to go through and still allow a certain degree of exploration; the only drawback is that the last lab session of the course should comprehend a tour of Cloud Vision API along with an AutoML example according to the Coursera page, while in the lab itself only the latter is present. I would suggest adding it back or updating the description.

Clear and informative videos introduced me to several new products. "cut & paste' Labs three starts. Lump first three into first, & two ML labs into one, & develop new ones that require students to write their own apps. The extra 'exploration time' on early labs was useless to me because did not have permission to set up a 'hello world' app. or access most of GCP options. Would be nice to provide lots of extra 'challenges' (w/tips, got FACE_DETECTION but never could view jpeg files)

This is super important and interesting course. However, I'm not happy with pace of the course. Instructor's narrative is slow (I've found it can be heard at 1.25 speed without loosing a topic). Another "complain" is how topics are presented, author expresses too much personal experience on them. This is good and interesting, however, when you are on Coursera's deadline, you need to be material oriented. This is not my first course, so I can compare this course with other ones.

I really enjoy the instructor's way of explaining things and the content was basic but clear. I found the course a bit basic for my skillset so I would like to have some challenging alternative in the labs. More hands on. Example: instead of telling me the command line to import a file into Cloud storage, provide an instruction such as: using gutil upload the csv file into Cloud Storage. (hide the solution and allow me to see the solution if I want/need to)

"GCP Big Data and Machine Learning Fundamentals" provides a good overview of the GCP ecosystem and pushes a compelling case for adopting their "no-ops" managed services. The course provides labs that will familiarize you with each component of the ecosystem but only slightly beyond the extent that it gives you a feel for the "administrative" steps of using GCP. It defers to Google's Data Engineering courses for more depth and implementation practice.

I wish the labs were more polished and that they built on each other (i.e., that Lab 3b doesn't start with doing everything in Lab 3a all over again). It can be tedious to redo everything. The buggy scoring module connecting Qwiklab and Coursera is a distraction. The content of the videos is very helpful in getting an overview of the GCP platform which can be very intimidating at first with all of the different brands to keep track of.

Content is very good, but as a non native english, it's very difficult to listen to the speaker. He has a very strong indian accent. It's something any speaker should work on before recording such videos.

Second, it would have been more professional to show better preparation, avoiding repeating himself. Sometimes the best is to reshoot some missed parts. It's an training course !

And please, stop saying the word "basically"...

Thanks to this course that I got to know, how GCP reduces the time and ops required to do advanced data analytics and build scalable Data Science products. Detailed differences between the same class of products GCP has to offer such as (Cloud SQL, BigTable, DataStore etc.) can add cherry on top of this course. Also, case studies in class with some practical assignments can be a good addition to the course.

it is good course but some of the instructions for labs are not clear. Hope this would be a credited course. I took this course for two reasons. One is to get paid for the work have been doing as unofficial consultants to various organizations online and to learn and improve my knowledge on Big Data and ML. Hope this course would help me to explore more opportunities at Google and other organizations

You should take this course if you already have a good understanding of ML and Big Data. As a law student, I was struggling with every step and every used term during lectures. Labs and module exams are muuuch easier compared to the courses. At times it did feel like a long difficult to understand commercial. However, if you look past all that there is much to learn here and benefit from this course.

very good introduction; sometimes going too much into a sales pitch of how great Google products are, but nonetheless very useful to get an overview of GCP. The Labs were a bit too guided (you basically just have to follow instructions and copy/paste ready made queries), but considering this is a high level intro, they fulfilled the purpose (getting you acquainted with the functionalities of GCP)

thank you for hosting these lectures where I benefit a lot. It could be better if

1. combine several lab session into one and avoid the waiting for opening the cloud lab. (5 min for each);

2. It might be more helpful to have more (graded/ with feedback) practice on Jupyter notes. The course seems designed more about building concepts rather than helping students pick up the skills.