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Вернуться к Smart Analytics, Machine Learning, and AI on GCP

Отзывы учащихся о курсе Smart Analytics, Machine Learning, and AI on GCP от партнера Google Cloud

4.6
звезд
Оценки: 1,077
Рецензии: 127

О курсе

Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs....

Лучшие рецензии

HM
11 мая 2020 г.

It was a good decision to do this course as i learn and practiced lot in GCP. Thank you the team for amazing support guidance and instructions. Course content and material was appreciated. Thanks.

MB
15 мая 2020 г.

Very good ML course to introduce students with Google Cloud machine learning capabilities. Maybe there should be a lab for AutoML (after video lessons), as it exists on Qwiklab platform.

Фильтр по:

76–100 из 126 отзывов о курсе Smart Analytics, Machine Learning, and AI on GCP

автор: Muhammad Z H

6 июня 2020 г.

Thanks

автор: Srikanth A

20 сент. 2021 г.

Super

автор: ENUONYE D J

25 нояб. 2021 г.

good

автор: Shweta M (

26 дек. 2020 г.

BEST

автор: Gerardo F V

23 нояб. 2020 г.

good

автор: Priyanka C

27 июня 2020 г.

Good

автор: CAMARA

20 февр. 2020 г.

:)

автор: harish k

11 мая 2020 г.

The course content was excellent and at the right level for me. I was able to complete my labs within the time duration allocated for each lab. The only reason I didn't rate this 5 star is because sometimes some components (e.g. Data studio) didn't launch correctly or I had to rework a couple of times.

All in all it was an excellent course. Thank you!

автор: Prashanth R

23 янв. 2020 г.

I couldn't complete the Kubeflow lab due to issues that I encountered setting it up. Overall, the course has given me a good understanding of Machine Learning model creation options available on GCP

автор: Michał R

25 окт. 2020 г.

In general - very hot industry knowledge, however it feels that this content needs some time to get flawless and robust as the other ones from this specialization.

автор: Federico M

5 мая 2020 г.

The course is good but there is a lot of repeating concepts from the first course of the data engineering specialization and some labs are a bit buggy

автор: Sandeep M

5 апр. 2020 г.

Course gives basic overview on ML concepts and how we can do using GCP. Good enough for Data Engineer to understand

автор: Alfonso1 C

31 мар. 2021 г.

Great hands one excercises to confirm few coding lines to do real world predictions

автор: Gaurav B

27 мар. 2020 г.

Few important concepts like kubeflow should have been covered in a bit more detail.

автор: Etienne M

24 апр. 2020 г.

I learned how to use Auto ML and its benefits. The tool looks like a black box.

автор: Yokesh N

22 июня 2020 г.

Great learning. Thank you for the wonderful videos and assessment labs

автор: Pascal A S

3 июля 2020 г.

Solid module. The Kubeflow Qwiklabs would benefit from improvements.

автор: Andrea R

1 мая 2020 г.

nice introduction in the world of AI in gcp. Relatively short though

автор: Kimberly S

9 нояб. 2020 г.

Running AI models on Kubeflow - this lab is long but interesting

автор: Carlos D A

31 июля 2020 г.

Great course, except many of the labs were on cloud shell only

автор: Hugh L

12 мая 2020 г.

A lot of material, requires careful review and study.

автор: Hiromu A

4 мая 2021 г.

It was good course to see the overview of ML on GCP.

автор: Imran R

21 мар. 2021 г.

This course help me understand AI, ML.

автор: Y C

25 сент. 2020 г.

would be nice if it could dive deeper.

автор: Rubens Z

10 июня 2020 г.

I missed Dataproc approach