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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.

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.

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).

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.

Great learning experience with this course. Using Qwiklabs on Google Cloud Platform made me introduced to alot many things and interacted with Big Query, creating Notebooks, creating VM instances using Compute Engine, using APIs to test existing APIs by google such as Text, Vision, Speech, Translate APIs. How heuristic rules are not necessarily need not to be followed while working on ML Model at certain point.

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?)

This course was well organized. I really loved it and I want to take the other courses in this specialization. thank you a lot for helping me like that and give me this opportunity to learn about ML.

I hope that you make other courses so I can enrich my knowledge, because I would love to work in ML field .So again thank you so much

the equality of opportunity is important but you are seeing less people discuss it when they work on ML project. It's very inspiring for me that it's important to consider this important factor when you design a model. Not just aggregating all of the data together, running into same algorithm and maximizing the profit.

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.

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.

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

!!

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.

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.

It was really nice and informative course and it turned out that I was really didn't know about all the risk and abominable topics in Machine Learning like the ML surprise, secret sauce all these funny though important terms have taught me a lot of real and formidable things. Thank you.

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

The course is little difficult for a person having little knowledge on computer especially cloud computing and programming. But the course contents are easy to understand. Only the Laboratory sessions have to be explored more by the participants for gaining more knowledge.

This course, as an introduction to the specialization, allowed me to land the ideas I had about machine learning, as well as focus efforts on obtaining the data that in the development of ML, now I understand what processes I must carry out so as not to fail in the I try.

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.

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.

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.

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.

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".

A nice overview of Google Cloud Platform's machine learning capabilities, and an intro into how Google thinks about Machine Learning. Could probably be covered more quickly, but gives you a chance to play around a little and explore if you wish.