Good Course

really good

Execellent

good entry

Loved it

not good

too easy

great

goood

good

Good

Nice

good

Nice

N

Its not bad. Its not so much a machine learning class, but a google cloud platform (GCP) class with a slant towards those who will be using GCP to do machine learning. PROS: GCP has a lot of parts and due to the large set of capabilities it can be overwhelming. This class orients a new user to GCP so they can build and use machine learning api's with and without tensorflow. Yes, the tensorflow is behind the apis, but other than the simple demo it is just showing the user how GCP can host a notebook and run some code. Perhaps I'm forgetful but the google crash course available for free will show you how to use tensorboard but this one does not. TLDR; If you know nothing about GCP and you want a quick intro to GCP for its ML offerings this is a great class.

Spent hard earned 50 bucks to get sucked into a week's worth of glorified commercial of black box services provided by the black hole that is Google. The only thing that is missing from these ads is the pricing. Helplessly hoping they tone it down in the subsequent courses of this specialization. Would really like to blame it on these companies that won't even check you out unless you've done stuff on Google's or Amazon's money maker boxes. And to the brother in the video about ML bias, the least you could do for your people is be Google'ly incorrect and talk about a "hypothetical" ML model that labels a black male in a sedan as a threat to a racist cop's life, rather than settling for the mild, plain old credit history example.

Too much very abstract information. Maybe there is something wrong with me, but at the point where I do not know anything about machine learning, I want to start learning those models, algorithms and all these fundamental things instead of spending my energy learning how to create an infrastructure of something that I do not know how to build. Maybe in future courses all this knowledge will find its place but currently I do not feel so.

A very good introduction to ML as Google sees it. A lot of focus on using Google products to solve ML problems and the various options Data Scientists have on Google cloud Platforms. Overall Good content but certainly room for improvement, to deliver a coherent story across the course and focus on the Why's (ML principles & logic) of doing things vs. the How's (Google's platform) of doing things.

This course lacks high-level organization, and the videos are not well organized in the Coursera portal compared to other courses. Also, the level of detail is very superficial. This is good for beginners or business/marketing people, not so much for advanced programmers. The assignments are also too easy, since every step is pretty much done for you and they do not promote independent thinking.

Great tour of the kind of things that GCP enables. Would recommend that you ensure that you have a github account and familiarity with Jupyter Lab before you start so that you can take notes and a small code base with you after you finish the course. I expect and hope that the remaining four courses in the GCP for ML specialization will give more implementation level confidence.

I found the beginning of this course a bit boring as it described a lot on google's strategy and organization on the subject. The second half of the course though, introducing datalab and python notebooks was exciting and very good to start playing with GCP services (compute and storage)

Presentation were good and I love speech of experts and I love to listen them specially demo part by Sara but one thing which I don't like is I feel more like marketing and promotion. Overall Good. But I feel more focus should on practical part too.

Qwiklabs did not connect to all services on GCP properly. I had to setup my own environment. This should be fixed to ensure a better learning environment. Apart from this it provides a good intro to Google´s ML approach & strategy.

This course is just ok. It is not interactive and I don't feel that I learned much when compared with other ML courses. I expected to work through the lab assignments rather than to simply click through them.