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Learner Reviews & Feedback for How Google does Machine Learning by Google Cloud

4,620 ratings
712 reviews

About the Course

What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<...

Top reviews


Nov 06, 2018

Great to know how to do machine learning in scale and to know the common pitfalls people may fall into while doing ML. Provides great hands-on training on GCP and get to know various API's GCP offers.


Mar 21, 2019

Really easy with all instruction.I didnt feel bored at any point gave me the basic idea of what is machine learning and how easy google made API's and cloud platform for machine learning\n\nThank you

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Dec 23, 2018


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Nov 16, 2018


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Oct 18, 2018


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May 31, 2018


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Oct 20, 2018


By Brad B

Aug 06, 2018

Very exciting to be able to learn this directly from Google and get introduced to QwikLabs and Cloud Datalab... the process of accomplishing the labs does seem to be cumbersome, but perhaps the details will be explained later (how accounts are processed, APIs activated, credentials... how data and compute engines are kept on standby or persistent once setup...). I have had some difficulties with getting the labs to work at times... couldn't see the Webview widgets at one point, because I didn't realize that I had to close the menu bar on the left side of the screen.

I have very much appreciated the overview introduction to the Google approach to ML... that is very helpful... knowing how to choose processes for ML and an appropriate approach to them... also keeping the scope of what they do somewhat limited and breaking it into pieces.

Having some experience with SQL and Python are very helpful for completing the labs and while I have had some of both, my skills are rusty. I might go off and study both of those topics and then plan to come back to this course, but I will see how the next week goes.

Thank you.

By Ang K K

Sep 13, 2019

Lak gave very good presentation in other lectures. However, in the first few lectures of this course, Lak was looking at the computer instead of the camera. It will be good if Lak can redo these lectures and look at the camera and hence maintain eye contact with the audience. In addition, if possible, try not to sway too much as it is very distracting. Overall, course is informative despite other comments about advertisement for Google. This is about how Google does Machine Learning after all.

By Liang-Yao W

Sep 09, 2018

A good introduction to how machine learning applications/products work in big companies like Google, and how path toward ML would look like. First hand experience from google should be valuable. Also get to try out google cloud platform. One star off because some of the exercises become nontrivial to follow because some user interfaces have changed since the course was recorded (but can still be completed with some try).

By Russell H

Aug 13, 2018

Content was really interesting. I especially the emphasis on the big picture (the 5 phases) and the discussion of bias in ML; I have not seen these in any other course so far. The labs were a bit involved even though no code had to be written, due to the number of steps and screens. The fact that the GCPUI has changed since the videos and instructions were put together added an extra layer of obfuscation to the labs.

By Tom

Aug 13, 2018

Very informative stuff. The videos could be better though:

a. a pointer following the data the lecturer talks about

b. more visuals

c. better explanations for concepts, e.g. the questionnaire about "create concept for ML Model for a chosen problem" was hard to understand, as general concepts were missing (e.g. how was the question "what would the API look like?" meant?)

By Shah M H R F

Feb 04, 2019

It is a very interactive course that helped me understand some amazing concepts of Machine Learning, BigQuery and google services available for the same. Understanding the APIs provided by google such as Vision API, Translation API, Speech API to name a few, were really helpful to my learning of ML models.

By Deleted A

Jun 23, 2019

Amazing course, I got to know about many contemporary things about Machine Learning in this course and also some about how Google does ML. But one thing is there, I had a trouble with getting access to the labs which usually says to me 'Sorry, access not granted to the resource', that surely sucks.

By Anubhav S

Jul 24, 2019

I am a student at a college and for me, this course makes a nice start. Although I find some things to be quite far fetched i.e some keywords we hard to understand.

I did understand the business aspect of this course and save it for my future years. This course makes me hype about the further one


By Javier C

Aug 19, 2018

Good essentials and introduction to Machine Learning. It doesn't go in depth of math behind, instead, it keeps it in a low level explanation for understanding basics of ML. They introduce how Google's APIs may be used for ML, and they seem really easy to use and get with them in production.

By Joshua B

Sep 11, 2019

I enjoyed the theoretical discussions, including the full process discussion, surrounding ML. Realizing that ML is not a panacea for random problems was a game-changer in focusing our efforts on problems that could benefit from ML now and defining the requirements for ML on other projects.

By Vishal V

Nov 03, 2018

A well put out course. The content flow was great and the Google product info seemed more contrived than ads. The lessons could have been a little more detailed and the content split into 2 weeks or so. A good course if you take it after Andrew NG's Structuring ML Projects course

By Saibal M

Jun 13, 2019

It's a part of the specialisation, so taking this course is important for completing the specialisation. But the contents of course is also important for a beginner, who is just going start his/her career in ml. It teaches some useful key points on doing ml in real life.

By Shridhar H

Jun 30, 2018

The course was excellent. It cleared some myths about Machine Learning. I always though that training the ML model was important but collecting data is much more important than that.Looking forward to use Google Cloud Platform in the future for production of my projects.

By Sinan G

Aug 03, 2018

Interesting new insights about biases in ML and a little about how to avoid it, thus going towards so-called "inclusive" ML. Nice overview about how to frame an ML problem and the key importance of data. Good and quick review of some of the Google Cloud API services.

By Ashutosh G

Aug 03, 2019

With focus on how to implement ML in the real world the course provides us with at least one part of the ML Pipeline which can allow an organization to quickly implement ML in its business. I look forward to the data ingestion part of the specialization.

By Nagaraj S

Dec 29, 2018

Excellent material on what are the pitfalls to avoid while doing machine learning. The laying groundwork and following all the steps section made sense. It shows that the course material has been prepared by practitioners - they "know their stuff".

By Jayank M

Jul 06, 2018

At some points it was a bit confusing. Especially the labs, where some instruction were not at easy to understand as the could be. But overall I found this course very helpful in understanding what ML meant and how we should approach it.

By Zhenyu W

Jan 02, 2019

Overall the course is good. IMHO the quiz needs to be improved. It should have more questions and the questions should be more directly related to the lecture or the contents. And I think the course should add some case studies.