End-to-End Machine Learning with TensorFlow on GCP
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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 <<<Get a Reminder
| Rating | 4.3★ based on 150 ratings |
|---|---|
| Length | 4 weeks |
| Effort | 3 weeks of study, 8 - 10 hours per week |
| Starts | Jan 24 (98 weeks ago) |
| Cost | $49 |
| From | Google Cloud via Coursera |
| Instructor | Google Cloud Training |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science |
| Tags | Computer Science Data Science Data Analysis Software Development |
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What people are saying
machine learning
It is an essential course if you want to build real life machine learning Models Good course with complete development cycle Great course!
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 Good overview course Definitely adds a unique perspective on thinking about machine learning systems at scale.
Super, resume la especialización 1 This course is more suitable for learners who have some prior familiarity with machine learning.
Overall, the course was good, but I would recommend previous experience with machine learning to avoid stalling.
I would recommend this course to anyone who is interested in learning about production machine learning pipeline using TensorFlow.
A comprehensive tutorial that walk your through the whole end-to-end process of machine learning.
Hope there is more similar courses like these where we can get our hands dirty with practical end-to-end machine learning case studies!
Awesome Course ..... A lot of great knowledge on enterprise implementation of Machine Learning Cloud Environments This first course of the specialization allows to jump from the ML model that one can play in its own computer, to the model that you can scale to become business operative.
One of my favourites Courses from Google, it provides an excellent overview of the End to End Machine LEarning process, and how to use GCP to speed up your workflow, highly recommended to anyone looking to expand their understanding of Real World application of ML Good the rare course where you can learn this topic today Great overview and recap on GCP ML.
Spent more time with Google tech support trouble shooting why their courses didn't work, than I did on machine learning...
good insight to GCP, even learned new concept of ML This was an awesome journey while learning the process of training the machine learning model on GCP The labs were completed, I need to only run them.
pretty good for intro to get a feeling of how the Machine Learning System is working as a product.
This specialization consists of 5 courses:Course1: End-to-End Machine Learning with TensorFlow on GCPCourse2: Production Machine Learning SystemsCourse3: Image Understanding with TensorFlow on GCPCourse4: Sequence Models for Time Series and Natural Language ProcessingCourse5: Recommendation Systems with TensorFlow on GCPIn specialization's FAQ say nothing about "audit" option.
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end to end ml
Practical Labs are really useful in learning end to end ML in GCP nice course Labs are not working.
very practical course for end to end ML This is a great start for advanced ml on gcp.
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google cloud
Would be nicer if students can use the google cloud with less restrictions.
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.
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.
Moreover, how to copy these notebooks from Google cloud repo on github is not mentioned anywhere.
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very well
The core concepts are useful both in GCP and if you decide to roll your own stack It was great course which converses entire ml learning journey Overall this is a very well structured and well delivered course i learned a lot from it.But I couldn't reproduce some of the examples on local machine so 4 stars for now.
The course is very well managed and very well delivered.
Very well formed course content
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with tensorflow
Good course to get hand-on experience on end-to-end ML systems with Tensorflow.
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Research Scientist-Machine Learning $55k
Cloud Architect - Azure / Machine Learning $75k
Watson Machine Learning Engineer $81k
Machine Learning Software Developer $103k
Software Engineer (Machine Learning) $116k
Applied Scientist, Machine Learning $130k
Autonomy and Machine Learning Solutions Architect $131k
Applied Scientist - Machine Learning -... $136k
RESEARCH SCIENTIST (MACHINE LEARNING) $147k
Machine Learning Engineer 2 $161k
Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
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| Rating | 4.3★ based on 150 ratings |
|---|---|
| Length | 4 weeks |
| Effort | 3 weeks of study, 8 - 10 hours per week |
| Starts | Jan 24 (98 weeks ago) |
| Cost | $49 |
| From | Google Cloud via Coursera |
| Instructor | Google Cloud Training |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science |
| Tags | Computer Science Data Science Data Analysis Software Development |
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