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Opiniones y comentarios de aprendices correspondientes a How Google does Machine Learning por parte de Google Cloud

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What are best practices for implementing machine learning on Google Cloud? What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code? What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently: it’s about providing a unified platform for managed datasets, a feature store, a way to build, train, and deploy machine learning models without writing a single line of code, providing the ability to label data, create Workbench notebooks using frameworks such as TensorFlow, SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability to train custom models, build component pipelines, and perform both online and batch predictions. We also discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important to not skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them....

Principales reseñas


5 de nov. de 2018

Great to know how to do machine learning in scale and to know the common pitfalls people may fall into while doing ML. Provides great hands-on training on GCP and get to know various API's GCP offers.


20 de mar. de 2019

Really easy with all instruction.I didnt feel bored at any point gave me the basic idea of what is machine learning and how easy google made API's and cloud platform for machine learning

Thank you

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1026 - 1050 de 1,092 revisiones para How Google does Machine Learning

por ashish t

13 de ago. de 2018

Company advertising and not machine learning experience.

por Shanaya M

7 de ene. de 2019

Good introduction to Machine Learning for beginners.

por Albert R A

19 de may. de 2022

Me esperaba un curso más práctico y menos teórico

por Bhavesh N

5 de may. de 2020

some of the content is same as other course


25 de abr. de 2020

Graded assignments are not much helpful.

por Luiz C

29 de jun. de 2018

lots of marketing for Google products...

por Nigar A

3 de oct. de 2019

too much theory instead of practice((

por Abdalla M

2 de oct. de 2018

contains many not updated parts

por Javier V

10 de oct. de 2020

Mostly google advertising

por sayan g

11 de sep. de 2019

not clearly explained

por Dragomir C

1 de nov. de 2018

More exercises, pls.

por Ahmed M M

4 de nov. de 2018

was a bit boring

por Ata M

13 de feb. de 2019

quick overview!

por Kaixin J

12 de jul. de 2018

Not very useful

por Chet

7 de ago. de 2018

Very Basic...

por Armann H

19 de jul. de 2018

A bit easy

por KimNamho

17 de abr. de 2019

thank you

por mustapha b

26 de dic. de 2019


por Tất T V

24 de jul. de 2018


por Imam S 0

20 de dic. de 2021


por JDBus W

10 de mar. de 2019

Feels a lot more like marketing, more than learning any actual ML. This is followed by some _very_ brief code tutorials for using Google's own well established ML models via REST API, which still feels more like a product demo than actually learning anything. Hitting a rest API isn't hard - although I feel like it's harder using the Python libraries you've chosen than making HTTP requests directly.

Either way, this doesn't teach any actual ML skills.

por Miwa E

23 de jun. de 2021

This much of contents should not take more than 3 hours. I wonder if the instructors are instructed to move as much as possible while they talk. It is the right strategy when one is on a stage, and the talk is live, but wrong strategy for recorded video lecture. I got motion sickness.

por vic c

2 de nov. de 2018

There is some good stuff here but I feel that I spent a majority of my time learning things that will be obsolete in a month. The labs were a disaster! I kept losing the server and often needed to define keys and obscure resources. This is NOT how I want to spend my time.

por Jay V

25 de oct. de 2018

It is tedious to have so many short videos, and only have the speakers face to see. A whiteboard approach with clearer explanations of what they're talking about would be much better. We need to connect the dots, not just listen to someone talking.

por Necip F E

16 de sep. de 2021

1​) Heavy advertisement by Google Cloud 2) Irrelevant quiz questions 3) Even a broken quiz question (The explanation for a test option was : "Correct. Sorry try again") 4​) Some actually useful advise + warm-up for GCP