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.

In essence, this was an interesting view into Google's methods, but can't help but feel like I've been advertised to instead of taught to. Obviously, their products are superior and really do provide an easy way to create a successful business that uses ML as a small or vital part, but for a beginner or a student, this is all irrelevant. The labs were presented without context or much explanation.

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)

The course wasn't very easy to follow as there were codes and various terms that were used which not everyone would be familiar with. This course is not for a beginner for sure but also not for an advanced student. The delivery was amazing though. The content was ggrrreeaatttttt!

The main speaker was moving too much and not engaging enough. The course materials could be rearranged to facilitate better use of the colab. There were a very accented speaker, I think from Europe, who had terrible organization of speech and also bad pronunciation.

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.

Some really good parts (like bias,) but generally a bit out of sync with the platform. Also, the quiz questions often seem to be out of sync with the videos; either questions answered later in the course or not covered at all.

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.

Muita teoria, pouquíssima prática e alguns materiais desatualizados. Mas em geral, ainda um bom curso para uma visão geral sobre ML. Ainda acho que um único vídeo de 40min resolveria todo esse conteúdo.

Some labs need to be updated (since GCP has been updated), but overall the course was informative. Also there is a quiz with formatting that makes it impossible to know how to answer. Please fix this.

This course was more of like everything about google, and what google did etc etc. and less focused on actual teaching.

Even though I am going to take the next course and will see what i get there.

A lot of repeated comments from the presenters that makes the course longer than needed. I know it is the beginning of the series, but it seemed like a sales pitch instead of a training.

Good course to get started. I personally didn't like the idea of using sandboxes for teaching instead I would have appreciated to use our own account with the free 300 dollar credit.

Very basic and short.

Some good information about the ML APIs, but very, very easy to pass and all code is copy and paste. Calling this Intermediate is a big stretch.

It could be better if lab challenges were described in more detail. For image characteristic what should be the type, from where it can be known was not explained.