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This was overall a helpful course but could be improved. It was nice to get some hands-on work with the Google Cloud tools but I felt that the instructions were confusing that that the video of the lab should have been shown before the lab rather than after. The presenters were somewhat uneven, it seemed for them this was a training course in how to make presentations. There was a high focus on doing what was ethical in terms of analyzing data. This seemed to constitute 30-40% of the materials. I agree that understanding bias is important in the analysis. Still, until I know how the tools work, it seems hard to know how to anticipate the potential biases. Seems that Google was trying to play to a different audience than the people taking the course in this regard.

The course was good in its contents.I learned some parts & APIs of the Google Cloud Platform which helps to create ML and AI enabled products for businesses. The course helped me to understand how can we implement the AI & ML for any business and get insights from the business data and eventually help the business to transcend. The Course explains various products and APIs offered by Google Cloud Platform which we can use to implement ML and also provides the Hand-on lab to practice some of the GCP APIs.

But in my thoughts, the course lack in explaining the basics of the GCP APIs used and someone who is new to GCP might find it difficult to understand how GCP APIs work from the backend (but nevermind, as it is the out of the scope of this course).

Some useful parts, but this course doesn't know if it's for project managers or programmers (evidence: the part where they expect you to write tensorflow code). I would advice the team behind this course to split it into 2 separate tracks, and to do deeper on the project management side. Unrelated to my given review score, I find it hypocritical that Google are teaching people about ethical AI practices, while at the same time (e.g. by firing their own AI researchers Margaret Mitchell and Timnit Gebru following critical publications related to ethical AI).

Overall liked the flow.

-May be some more examples/labs as well as a case study of a company went through their business transformation with ML would be helpful.

-Some interviews with leading practitioners of ML as well as business leaders would be great.

A module with specific advice for product managers, designers, C-executives, etc would be helpful.

-Bringing in few solutions from non-Google would make course more complete.

-Resources to follow-up would be superb.

Although the videos include very usefull information, sometimes a felt a lot of advertisement of google cloud products (which maybe I should have expected)

The first graded assignment was excelent learning experience with dialogflow but with the others I felt just like following a bunch of instructions.

decent upto speed course, collects good expanse, the hands on labs are the best thing about the course; doesn't go in depth or charts path, course with additional learning resources: area where this course can improve upon maybe; all in all great course for anyone not at all technical

The virtual assistant excercise was qutie cool, because you could could easily apply it to a case that is close to you. For most of the other excercises with pre-set datasets and code, I did not learn a lot by just copy and pasting. I understand the google AI services better though.

Decent intro for someone you did not know what ML is. But in no way do I feel prepared to use it. Only 2 labs showed how to use the google cloud platform, but they did not show how to use your own data, or even how to collect data, just provided the data for you.

Fix the automl graded test on week 2. After multiple emails with no proper answer, I found the solution to the bug through the forum. Add -vcm after the project id for the bucket creation. It frustrated me that much that I almost lost my interest in the class.

The course is good with few hands on tools that can be used for managers. However, many videos ended abruptly without any continuation. Also, the assessment shud be more comprehensive, each week only some 3-4 very simple questions were there.

Some module is very educational and easy to follow (and some is not as much). Lab sections can at time be confusing, try to look for things within GCP. More details (screenshots) how to navigate GCP will be a huge time saver!

Provided a good introduction of the course. However got a bit lost in Week 3 for the deep discussion and lab on data structure and analytic which i have no background. Some more background details to introduce can be better

Basically this course for one who wants to speak the same language as your manager, which ends this course. Will be helpful for startups who don't know how to configure a cluster and launch ML algorithms .

The course definitly gave some good insights on ML. And is definitly a great course for someone relatively new to ML. But the "Quick labs" though requiring no coding, the concepts only scraped the surface.

It was ok. I am more on the technical side so it was completely a new way to look at ML for me. Also, some of the use cases depicted helped me understand up-to what extent ML can be used

Horrible Instructor in week 2&3 could hardly follow him even gave his videos a thumbs down.. other instructors were good thanks to them I was able to follow and finish the course

This is an excellent course for someone who want to get a gist of Machine Learning and various use cases around it to kick their imagination and implement ML in their business.

Out of date, too abstract and Google focused (well it is offered by Google). Does not fill in gaps well. But a good introduction to Google's paid Cloud Platform.

It had excellent professor & teaching style was very nice. but the quicklabs was very slow it take more time to load and also show it required high internet

Good course, but too oriented to the use of google cloud, I would have liked to see more applicable technical information without the need for such a tool.

It's not what I want to learn and it's kind of boring. Also, I have no idea how to unenroll this course, the three grey dots only showing "rate course"

The course has good content but few practical excercises. was promizing but it would have being better having more quicklabs activities.

you did not cover how to actually use whole ml like how to acquire data and how to go for visualization of data but all over is nice.