This is too obviously an advertisement for google services disguised as a course.

The omnipresent Google branding on software used is truly sufficient publicity in an educational setting, the branded t-shirts and multiple statements are superfluous for a Coursera class. Many statements, especially towards the first videos, create an impression that the technology isn't superior enough to sell itself as we learn it, but requires a sales pitch even though we already subscribed to the specialization on that particular Machine Learning software.

The notebook based demos are unfortunately pretty useless as labs. All of these courses would be much improved with real labs that require the student to build the system.

As an overview this course is OK. But the quality of labs can be improved.

nice practical overview. I liked the first part with practical examples, the second part is too much marketing for Google APIs for me

This course lays the foundation of Machine Learning.

Some assignments were not quite useful as it involved just running the programs

Though the course provided has some good information for beginners, this is moreover like advertising google products.

This is already available on YouTube for free. While you are charging for it. Secondly, it is so basic that it is of no use.

Terrible, no challenge, lots of marketing speak and coping and pasting...

Best ML course with google style..starts from detail explanation from the coverage of full of presentation with examples.Like the way of coaching..Easy to understand..thank's google and coursera..

It is very informative about the machine learning and AI usage in Google products and provide deep dive into GCP platform in very intuitive way. I recommend to AI aficionado.

Thanks you Google!!!

This is by far the worse course I've ever had - the videos are too many and the majority of them are too short. There are like 5 different lecturers for a bunch of 2 minute long videos which is unnecessary. The lab exercises were boring and not challenging. Overall I think I could have learned more by reading 1-2 blog posts. I hope the rest of the courses from this specialization are better organized, more interesting and challenging.

The course reminds me of a movie trailer, do not expect anything more than a preview.

Ideas are presented at a high level and exercises consist of copy and paste commands with no explanations. At the end of the course, you are left with very few insights and no new skills.

This is not a course! It is just an ad to Google services. One does not even have access to the rest of the courses in the specialization without paying.

So You have to pay in order to learn how to pay for Google Cloud APIs.

Felt like a long advertisement

This may be the most important Machine Learning course I've ever taken. This course is not about theory or technical details, this course is about how to get ML working for you in your business. Google shows us the business processes that they have used and helped other companies use to be successful in getting real value out of ML. If you have tried ML in your business and not gotten any traction, you may have fallen into some of the pitfalls discussed in this course. I look forward enthusiastically to following the recommendations in this course and championing them throughout my company. In this course Google shares real-world solid practical business experience on how to go about the end-to-end process to make profitable use of ML. I give this course my highest rating, 5 STARS!

Trying to run the Code from a local machine was a little more of a challenge than I thought it would be. Here's a code snippet that hopefully will help others in the future when they attempt to do the same.

#For this to work change __init__ in Programs\Python\Python37\Lib\site-packages\httplib2 line 1090 from int to float

APIKEY="AIzaSyD4EXe8wuo38HlDLYcmr9zUT5JBXo7uY00"

# running Translate API

from googleapiclient.discovery import build

service = build('translate', 'v2', developerKey=APIKEY)

# use the service

inputs = ['I love words that aren\'t']

outputs = service.translations().list(source='en', target='fr', q=inputs).execute()

# print outputs

for input, output in zip(inputs, outputs['translations']):

print("{0} -> {1}".format(input, output['translatedText']))

Very good course about production machine learning. I take this course after taking the Andrew Ng's course, as I found MatLab was not very popular among the community, and tried to learn some Tensorflow stuff. The first course in the specialization is a surprise. Very deep insight on machine learning in real world taught by Googlers. The serverless concept is also fascinating as I am facing issues with running ML stuff on my own. Recommended this course to people who wants to understand the design and business application part of ML.

Invaluable introduction on PRACTICAL machine learning. The meat of this course is how to think about production ML services. If you are tired of endless algorithm design (or are less technically inclined), this course will introduce you to the mindset of thinking about problems from an ML (data) first perspective. The labs section at the end where you get to experiment with GCP felt a little rushed. I know it was just meant to be a teaser. Looking forward to going deeper in the next courses.

The course is good and very well depicted by the title "How Google does ML." You see a lot of real world examples/applications, obviously all provided by Google products. Moreover, they introduce a big picture of a machine learning project, and even more so a whole scheme of an organization that is about to do machine learning successfully. Videos are totally informative and concise, straight to the point without a lot of unnecessary "hello/welcome/thank you, etc." I like it!

Very nice introduction to Google Cloud Platform: Compute Engine, Storage, Data Lab, Big Query, Google's amazing ML APIs. I'm a novice and dont have much Technical background nor Data Science but this course was easy to follow.

Labwork was really inclusive as it contained some real-life scenarios and made me comfortable that with little practice (and little Tech expertise), I can also do ML/AI. Amazing course and a must do for anyone who is interested to learn and do ML

Having got to know about GCP again and it really makes a difference when you just work withuot cloud and with cloud . Many factors such efficiency, chances not to fail , total work on logics rather than building a pipeline that has been already made increases. However in my opinion one should take GCP fundamentals on data engineering course after this one or before this one and then jump to the next courses in this specialization.

Thank You Google

This course is probably one of the finest curated introduction to an organizational ML solution standard.

We tend to overlook some of the critical phases and learning the processes and organizational know-how that GOOGLE applies to its ML solution development process would help me as an ML & AI engineer to not only revisit my skillset but also brush my application process for ease of deployment and for ensuring scalability within code .

This course really helped me to understand How ML can really create a huge impact in the world of technology.The instructors were really interactive , they explain the concepts with real time application which i find it very interesting . I would like to thank the Google Cloud Team for creating this course and I'm looking forward to join your other courses and specialize in Machine Learning .

Thank You All

I like this course but it is strictly teaching that the cloud to use it not the main ML into details. Like we know data they told to collect but we haven't performed any data handling or collecting exercise nor we have done any accuracy optimization. What we did just learnt how to use Google API's not what the inside code it so it's just marketing of google product which they are selling.