Chevron Left
返回到 End-to-End Machine Learning with TensorFlow on GCP

学生对 Google 云端平台 提供的 End-to-End Machine Learning with TensorFlow on GCP 的评价和反馈

4.6
786 个评分
121 个审阅

课程概述

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp). 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: https://qwiklabs.com/terms_of_service <<<...

热门审阅

SA

Mar 03, 2019

Definitely adds a unique perspective on thinking about machine learning systems at scale. This course is suitable for Data Scientists, Data Engineers and Machine Learning Engineers.

KT

Oct 07, 2018

A very helpful course, we were able to practically apply all the knowledge we received from the First Specialization. I feel much more confident to do ML after this course!

筛选依据:

101 - End-to-End Machine Learning with TensorFlow on GCP 的 122 个评论(共 122 个)

创建者 Gabrielwry

Jul 11, 2019

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

创建者 QZ

Jul 21, 2019

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.

创建者 Mohamad A

Aug 10, 2019

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

创建者 Mr. J

Sep 05, 2019

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.

创建者 Prasenjit P

Sep 16, 2019

Good !!

创建者 Ian Q T C

Jan 19, 2019

Exactly what it says. Labs are trivial and I felt like I didn't learn much other than how to use the interface for serving and taking a model from start to finish. The core concepts are useful both in GCP and if you decide to roll your own stack

创建者 David K

Mar 12, 2019

Good: Course structure = great, content is relevant and interesting

Bad: Labs do not always work (e.g. deprecated GCP modules incompatible with apache-beam), code for labs already contains answers... would be nice to have "lab" file and "answer" file to make learning more explicit, also, the white guy with the mustache should rerecord his videos.... the cadence is distracting and he does not go into as much depth as Lak

创建者 林佳佑

Nov 02, 2018

the course is helpful for any learner initial to touch GCP learning

创建者 KimNamho

Jun 12, 2019

thank you

创建者 Mark Y

Jun 22, 2019

nice

创建者 정은성

Jun 29, 2019

I am satisfied with GCP training except for some errors.

I think I need the latest update.

创建者 길경완

Jun 30, 2019

well

创建者 Rahul D G

Jul 13, 2019

QuickLabs has error in many labs

创建者 Pablo M P

Aug 25, 2019

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

创建者 신형재

Jul 02, 2019

errors...

创建者 Alireza K

Jul 27, 2019

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

创建者 Grzegorz G

Feb 12, 2019

Labs are not working. I'm getting 'access to the resource denied' error

创建者 Russ K

Aug 30, 2018

Looks like this class could be very useful, but you have to pay up front before you can try any labs. Don't bother auditing.

创建者 Sacha v W

Jun 20, 2019

Quite some labs did not work. It shows how several components of GCP can be used. Then there are implementation labs that are really abacadabra. I was really disappointing.

创建者 Jakub B

Jun 19, 2019

Very weak course. There are no assignments, only 'labs' for which there are walkthrough videos that tell what happens in code, but they don't actually ask to implement or test anything.

The qwiklabs platform is also very unwieldy - even for labs that take 10 minutes you have to go through set up that takes at least 5 minutes...

If you want to take whole specialization the course might be useful, but otherwise DO NOT DO THIS COURSE

创建者 bearrumor T

Jun 28, 2019

Too short Time and Too Many Contents and Too rare comments for ToDo items in LabTasks

创建者 Maxim

Jul 05, 2019

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