End-to-End Machine Learning with TensorFlow on GCP

It was very nice

This specialization consists of 5 courses: Course1: End to End Machine Learning with TensorFlow on GCP Course2: Production Machine Learning Systems Course3: Image Understanding with TensorFlow on GCP Course4: Sequence Models for Time Series and Natural Language Processing Course5: Recommendation Systems with TensorFlow on GCP In specialization’s FAQ say nothing about “audit” option. Do You know what is it ? “Audit” means that You can use course video material even after You subscriptions ended. By fact, only “Course 1” has such ability. Before pay for specialization, carefully check FAQ for EACH separated course in specialization: courses 2 5 has special point: “Why can’t I audit this course? This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available. ” “Who have paid” means that after You subscriptions ended, you lost access to video materials in this courses. p.s. 1 star only for “Audit”, content and lecturers are rated higher at least 4 stars

pretty good for intro to get a feeling of how the Machine Learning System is working as a product.

Excelente didatica e praticas para aprender fazendo. Muito bom.

The labs were completed, I need to only run them. which I think I couldn’t engage myself

good insight to GCP, even learned new concept of ML

Nice recap of all previously concepts and a great hands on with ML Engine.

It is good course it contains all required to understand what you need to make and finalize and I learn all steps needed to make model ML app with google. However, there some notes sometimes I miss understand in labs there moving in code fast without explain maybe the labs for us to read later and at the end thanks to share with us your expertise and information

it was really an awesome Experience

This course is not very practical nor very well explained. The topic is very interesting but it is not delivered clear enough.

great survey of it. optional labs should be mandatory I think. Also it would be nice to have a end to end walk through in summation. another option to complete the mental model it to map notebook sections to the GCP infrastructure in a presentation. I wonder about cloning the gcp repo locally to use it as a local template to further study. In other words I fire it up in my account later. or I access GCP via anaconda jupiter. Just wondering.

learned alot

Awesome tutorial

Good !!

It’s great if one wants to know actual deployment pipeline.

Learned a lot from this course on Data Preprocessing to Deploying a model and making predictions.

great

It’s good to quickly overview ML. But some syntax is not so friendly to understand if I didn’t see the manual .

Good course overall, maybe the labs are too short or too easy, maybe it would be better to have a link to a doc with some related tasks. Two labs were missing, one I could see while looking around in the notebooks, but the last one I didn’t, i would have appreciated since I don’t know flask yet

Course covers the fundamentals of GCP with TF. Although the labs don’t require much of a coding, and the ones which require have a poor structure because after each subtask say Task 1, you should be able to see if your code outputs the correct output, so for that they should have included some testcases. Also in the training part, quicklab has limit of 2 hrs, but training takes about 40 50 mins for a lower input size, and that lab requires to run training 3 times, so I was forced to just trim down the input size to fit all tasks within the lab time limit.

Most of this course lack the actual coding so I dont think I get much actual experience from this course

Without a doubt, a great recap of the last specialization.

Excepcional!!!

Good to work in a real world environment, though not much hands on work (most code has already been provided). Still not familiar with API’s details.

Good to work in a real world environment, though not much hands on work (most code has already been provided). Still not familiar with API’s details.

Really enjoyed the labs

woww, great learning.

great course must learn

Course content is good. But Coursera policy is bad. After completion of the course, only a 1 month extension is given to revise it after that. You cant see the content. This is very bad. It would be better to download the videos. How are you supposed to revise the contents? After that, you have to pay again. I will think twice before going for any course with coursera.

awesome learning experience fro the teacher from google. thanks to coursera and google for providing me such a good lesson which will be beneficial for my upcoming future and research work

Excellent! the great part of this course is access to labs where you can have hands on experience without any cost.

Course is very good, a lot of examples and high value videos

Course is very good, a lot of examples and high value videos

This is a very good introduction, I regret not being able to do optional lab 7 (no qwiklabs) an seing the app live

Amazing!! learning a lot!!!

I appreciate the content being on github but the course had many technical difficulties on GCP. Much of the content in videos was in python2.

Spent more time with Google tech support trouble shooting why their courses didn’t work, than I did on machine learning…

The labs could have been a bit more useful.

Hits the sweet spot of not trying to teach you model development or TF but still shows how to integrate with the GCP mindset.

More explanation and digging deeper into code would be welcomed

The course is very well managed and very well delivered. The labs gave an opportunity to learn the course from implementation point of view.

Notebooks for labs would not open sometimes. They would just spin and I wouldnt be able to open a new instances (using chrome incognito).

Nice course for practical implementation of Machine Learning.

Gooood

Excellent

Practical and succinct!

Interchanging Tutors and many repo files made it bit complex

Nice.