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Вернуться к End-to-End Machine Learning with TensorFlow on Google Cloud

Отзывы учащихся о курсе End-to-End Machine Learning with TensorFlow on Google Cloud от партнера Google Cloud

Оценки: 1,640

О курсе

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization ( One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform Prerequisites: Basic SQL, familiarity with Python and TensorFlow >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<...

Лучшие рецензии


20 сент. 2020 г.

I would like to thank Lak and Chris for their wonderful presentation of the deployment of ML models on the Google Cloud Platform. The case study problem chosen for the course is also unique.


17 нояб. 2019 г.

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

Фильтр по:

151–175 из 259 отзывов о курсе End-to-End Machine Learning with TensorFlow on Google Cloud

автор: Julio C M L

29 февр. 2020 г.


автор: Naman M

22 сент. 2019 г.


автор: Lee S J

2 июля 2019 г.


автор: Lee M

1 июля 2019 г.


автор: Gaurav S

25 июля 2022 г.


автор: Sathira R A S

8 мая 2021 г.


автор: Souvik S

13 апр. 2020 г.


автор: 황인규

3 июля 2019 г.


автор: 이전규

23 июня 2019 г.


автор: Osman D R T

15 июня 2019 г.


автор: Atichat P

3 окт. 2018 г.


автор: Manu G

4 окт. 2019 г.

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.

автор: Mr. J

4 сент. 2019 г.

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.

автор: Learn P P

20 июля 2020 г.

Great course. I knew about machine learning but didn't know how to make a production system. This course helped me to achieve that goal. Now, I am confident of the fact that I can work in this field and work in a company. Only thing which needs to be taken care is about the coding part. We don't get hands-on, though I realize it will be difficult to do it at first attempt and in limited time. BQML is also very handy.

автор: Samarth G

13 апр. 2020 г.

Good course. It gives a nice overview of how to build a ML model on GCP and deploy it to be used as a REST API. The labs could be improved. I found the lectures to be extremely helpful. The teachers do a great job at explaining the concepts. The labs give a hands on to what we study in the lectures. However, the labs could be improved. Some of the labs have issues that need to be fixed.

автор: Rohan S

24 февр. 2019 г.

This course is more suitable for learners who have some prior familiarity with machine learning. For people who are unfamiliar with the Google Cloud Platform, this course walks you through all the steps required to build a simple model on Google Cloud Platform. Overall, the course was good, but I would recommend previous experience with machine learning to avoid stalling.

автор: Mohamad A

10 авг. 2019 г.

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

автор: Aditya h

12 сент. 2018 г.

Good overview of end to end ML utilizing GCP starting from preparing the data set from Bigquery , utilizing data lab for building the model on a smaller dataset, Moving to Cloud ML engine to perform distributed training on a larger dataset, using Apache beam for pre-processing the data before serving and google app engine to finally serve the model

автор: Syncace

15 мая 2022 г.

because cloud services are changing so fast, the course is not up-to-date.

but I can still learn the concept of the ml end-to-end process for gcp cloud.

the course suply useful and efficient skill dealing with data and ml. we can use it not only in the cloud, but on-premises.

автор: Lloyd P

1 янв. 2019 г.

The qwiklabs interface to GCP is a little cumbersome. The need to start and stop sessions with each lesson wastes some time. I would prefer if the course came with a GCP credit and we were able to use our own accounts and still have a way to keep track of progress,..

автор: Jonathan S

13 окт. 2018 г.

It is an amazing demonstration of what Google Cloud can do in just a few lines of code, but a couple of the labs did not completely work for me, especially when it came to running jobs on Cloud ML. They were not essential, and the experience was still great.

автор: QZ

21 июля 2019 г.

The course is well structured. However, Google moves really fast when creating new products hence there is some confusion when running the labs. That being said, it's amazing that qwiklabs is utilising essentially a live environment for practice.

автор: Qi L

6 авг. 2020 г.

This course introduces the basic steps of working a ml project on gcp. It would be better to have more blank code pieces for student to write. Currently the notebooks are reading materials, instead of a hands-on project.

автор: vincent p

6 февр. 2019 г.

Needs more explanations about the performance.

I do not understand why processing is so slow.

it is dozens of minutes or even more than 1 hour to process a few gigabytes.

Datalab takes more than 5 minutes to start, why ?

автор: Mauro B

19 сент. 2018 г.

Interesting hands-on course. You can grasp the full workflow from exploring a dataset, select/validate and transform inputs, define the model, train and validate it in a small scale and on Google Cloud Engine.