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Opiniones y comentarios de aprendices correspondientes a Machine Learning in the Enterprise por parte de Google Cloud

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1,425 calificaciones

Acerca del Curso

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks. The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model. You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker. The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models....

Principales reseñas

MB

30 de dic. de 2018

thanks for the great work. There is so much to learn and I appreciate the effort you made to break things down and providing lab while making the hard decisions on what to commit.

MK

6 de jun. de 2020

This course is so really good to learn about the general knowledge and skill of Data Science like optimization batch or regularization and so on with Google Cloud Platform.

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101 - 123 de 123 revisiones para Machine Learning in the Enterprise

por Joel M

12 de dic. de 2018

good lessons and in depth coverage of a range of issues

por Hugo H

3 de abr. de 2020

Good course, pragmatic and full of practical exercises

por Attila B

20 de dic. de 2018

Really good course with a lot of practical examples.

por Pratik S

21 de oct. de 2019

complete hyper parameters is given in lab

por Ruslan A

16 de ago. de 2019

Many notebooks contain some typo/erros.

por Wang Y

29 de oct. de 2018

best course in the specialization!!!

por Gaurav B

13 de feb. de 2020

I was looking for more hands-on.

por Sarwar A

23 de feb. de 2021

Good course overall

por Swaraj P

10 de mar. de 2019

Nice tutorial

por Xenon

11 de nov. de 2022

Excellent !

por KyeongUk J

28 de oct. de 2018

great

por Matthew B

29 de jun. de 2019

Labs were very confusing. Explained theories well but in practice didn't really learn much. I wouldn't recommend if you're a beginner. Google has a very interesting way on teaching.... On that note they should stick to building tech, never teaching. Didn't really learn how to build anything in ML, sort of skimmed on some API's they offer. In reality, the first course was probably the best... The rest of the specialization was just a rinse and repeat sort of thing.

por Bhargav D

26 de abr. de 2020

Great course must should make labs compulsory and not provide solution it takes away the fun of thinking.

por Siddharth A

9 de nov. de 2018

I felt that hand-on or explanation was not sufficient. Coverage is good.

por Alberto C

23 de oct. de 2018

There are some lessons where the concepts are exposed in a too fast way

por Rahul K

5 de may. de 2019

Some tough concepts !!!

por Yakov F

18 de oct. de 2022

All eight labs had defects/ bugs. In four (4) labs the defects prevented me from completing the labs. There was no waiting to contact Google cloud support by chat, but the best the representative was able to do was to give Coursera credit for the unfinished lab, rather than to help find and fix the defect in the lab.

por Pablo I F

5 de ago. de 2020

Very bad english subtitles. For non-english speakers, the subtitles doesn't help, but it confuse what the teacher is explaining. It takes me a lot of time to understand some parts of the course

por Mike W

22 de jun. de 2019

The notebook based demos are unfortunately pretty useless as labs. All of these courses would be much improved with real labs that require the student to build the system.

por Arman A

11 de abr. de 2019

Pros: Tensorflow is an excellent framework for deep learning

Cons :

1- The way this material is designed is 10 X SHIT

2- Either teach properly or don't teach at all.

por Mohannad B

13 de ago. de 2020

A lot of inaccurate data, please check deep learning ai specialization for more accurate info. this is good for introducing you to GCP not the concepts of AI

por Radha M K V

29 de dic. de 2019

Very redundant and superficial.

por man c y

26 de jun. de 2019

poor labs