Mar 03, 2019
Overall a good curated course to help understand the GCP offerings and high level architecture of how their offerings fit in the current landscape. Easy to follow along as this was fundamental course.
Sep 24, 2019
This course really helped me in understanding exactly 'How the Big data and Machine learning can be used in Cloud' and 'The ease to use it'. Thank you for summing all the fundamentals in this course.
By James T•
Mar 11, 2017
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.
By Shuji M•
Sep 28, 2018
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.
By Nethra S•
Jan 07, 2019
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!
By Lauro O•
Jul 02, 2017
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.
By Wanda B K B•
May 27, 2019
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!
By Serge B•
Oct 06, 2017
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.
By Emanuele G•
Apr 06, 2020
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.
Sep 06, 2018
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)
By Anton A•
Sep 23, 2017
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.
By Ingrid J•
Nov 10, 2018
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)
By Evan P•
Dec 27, 2017
"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.
By Mike H•
Sep 22, 2018
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.
By Christian B•
Jan 12, 2018
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"...
By MAYANK G•
May 05, 2018
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.
By Padma E•
Mar 23, 2020
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
By Jianhong X•
Jan 12, 2019
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.
By Konstantinos S•
Jan 07, 2019
Overall a very good course. I would have liked a bit less "google is great" stuff and a bit more depth into their products, including an equivalence with AWS and Azure services. The labs were very interesting but some of them were repeated and a bit basic in the graded part (for instance the ML API lab tests whether I can clone a repo using a web interface and full instructions).
By Lin-Ying C•
Jul 18, 2020
This course introduces fundamentals on how to use tools on Google Cloud Platform (GCP) to deal with big data processing and machine learning applications. It mainly focuses on how to use GCP, which is great if people are not familiar with it. However, if one wants to know more about data engineering or machine learning, this course might not be a good choice to start with.
By Louise C•
Jul 16, 2020
I found this class to be at the right speed and length. Since I specialise in Machine Learning, the different methods to build a model were not new to me but I really enjoyed learning about the power of Data Proc, Data Flow and Pub/Sub and understanding more about the whole GCP architecture. I recommend this class to anyone that is a bit familiar with the GCP products.
By Eric L V•
Sep 23, 2019
The course was a big light in scope. The final lab also has a problem in that if you click on the AutoML bolt-on application URL (from the labs START/END LAB page), you loose your credentials page and ability to finish the lab. The verbiage around starting up AutoML need to be much clearer -- e.g. 'copy the AutoML URL here, into your incognito browser'.
By stefano d p•
Dec 30, 2017
materials could be updated to be in line with the labs. Moreover it would be nice to cut moments where the speaker got lost during the videos in order not to lose time (many times there were 20 sec of silence).
Other than that the course is great. It would be nice yo have more info about the certification (mainly about the structure of the exam)
By vinsent p•
Aug 08, 2017
The realtime examples of the cine industry image processing was really good to see the power of GCP.
Some more in-depth example of query the cloud storage, and G C function should have added weight-age to the course, other wise the instruction are good to start the Google cloud.
More examples of using the google CLI should have been added.
By Duncan T•
May 26, 2018
Better than I expected. I thought it would just be a massive advertisement, but there was actually some good and interesting content. I guess it helps when the api's your are promoting are really cool. The labs were helpful, but separate steps of lab 4 should have been combined. It took a long time for datalab to set up for a 15 min lab.
By Sonila K•
Aug 14, 2018
Bumping the score by a point due to great tech/customer support.
The lab instructions could be clearer and the real time update of scores is not working for few of the labs that needs to be corrected. Content was good.
By Pradeep D M•
Dec 05, 2018
The Tutor is very knowledgeable and provides a good mix of examples along with each chapter. However, the exercises are quite repetitive and seems like the gap between exercises and the lectures sometimes is too long. The user experience could be improved by using the same project across similar lab exercises with reduced setup time.