課程信息

100% 在線

立即開始,按照自己的計劃學習。

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中級

完成時間大約為5 小時

建議:1 semana de estudo, de 8 a 12 horas por semana...

巴西葡萄牙語

字幕:法語(French), 巴西葡萄牙語, 德語(German), 英語(English), 西班牙語(Spanish), 日語...

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為5 小時

建議:1 semana de estudo, de 8 a 12 horas por semana...

巴西葡萄牙語

字幕:法語(French), 巴西葡萄牙語, 德語(German), 英語(English), 西班牙語(Spanish), 日語...

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 11 分鐘

Este é o "Serverless Machine Learning on Google Cloud Platform"

...
2 個視頻 (總計 5 分鐘), 1 個測驗
2 個視頻
Considerações sobre machine learning2分鐘
1 個練習
Pré-teste do curso de machine learning6分鐘
完成時間為 3 小時

Módulo 1: Primeiros passos com machine learning

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21 個視頻 (總計 109 分鐘), 2 個測驗
21 個視頻
Tipos de ML3分鐘
O canal de ML2分鐘
Variantes do modelo de ML7分鐘
Como classificar um problema de ML2分鐘
Como usar machine learning (ML)8分鐘
Otimização9分鐘
Um playground de rede neural18分鐘
Como combinar atributos3分鐘
Engenharia de atributos3分鐘
Modelos de imagem5分鐘
ML eficaz2分鐘
Quais são os elementos de um bom conjunto de dados?5分鐘
Métricas de erro3分鐘
Precisão2分鐘
Precisão e recall5分鐘
Como criar conjuntos de dados de machine learning3分鐘
Como dividir conjuntos de dados6分鐘
Python Notebooks1分鐘
Visão geral do laboratório Como criar conjuntos de dados de ML3分鐘
Revisão do laboratório Como criar conjuntos de dados de ML2分鐘
1 個練習
Teste do módulo 18分鐘
完成時間為 5 小時

Módulo 2: Criação de modelos de ML com o TensorFlow

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15 個視頻 (總計 65 分鐘), 5 個測驗
15 個視頻
O que é o TensorFlow?5分鐘
Principais características do TensorFlow5分鐘
Visão geral do laboratório Primeiros passos com o TensorFlow7
Revisão do laboratório TensorFlow10分鐘
API Estimator8分鐘
Machine learning com o tf.estimator15
Revisão do laboratório Estimator7分鐘
Como criar ML eficaz6分鐘
Introdução ao laboratório Refatoração para adicionar a criação de lotes e recursos38
Revisão do laboratório Refatoração4分鐘
Treine e avalie4分鐘
Monitoramento1分鐘
Introdução ao laboratório: Treinamento e monitoramento distribuídos2分鐘
Revisão do laboratório: Treinamento e monitoramento distribuídos7分鐘
1 個練習
Teste do módulo 28分鐘
完成時間為 2 小時

Módulo 3: Escalonamento de modelos de ML com o Cloud ML Engine

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7 個視頻 (總計 28 分鐘), 2 個測驗
7 個視頻
Por que usar o Cloud ML Engine?6分鐘
Fluxo de trabalho de desenvolvimento1分鐘
Como empacotar o treinador3分鐘
TensorFlow Serving3分鐘
Laboratório: Como escalonar ML39
Revisão do laboratório: Como escalonar ML10分鐘
1 個練習
Teste do módulo 34分鐘
完成時間為 3 小時

Módulo 4: Engenharia de atributos

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16 個視頻 (總計 92 分鐘), 2 個測驗
16 個視頻
Atributos bons7分鐘
Causalidade8分鐘
Numérico5分鐘
Exemplos suficientes7分鐘
Dados brutos para os atributos1分鐘
Atributos categóricos8分鐘
Cruzamento de atributos3分鐘
Como criar intervalos3分鐘
Amplitude e profundidade5分鐘
Onde aplicar a engenharia de atributos3分鐘
Visão geral do laboratório Engenharia de atributos3分鐘
Revisão do laboratório Engenharia de atributos10分鐘
Ajuste de hiperparâmetro e demonstração15分鐘
Níveis de abstração de ML4分鐘
Resumo1分鐘
1 個練習
Teste do módulo 46分鐘

關於 Google 云端平台

We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success....

常見問題

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 您购买证书后,将有权访问所有课程材料,包括评分作业。完成课程后,您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

  • Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following:

    • Knowledge of Google Cloud Platform

    • Big Data & Machine Learning Fundamentals to the level of "Google Cloud Platform Big Data and Machine Learning Fundamentals" on Coursera

    • Knowledge of BigQuery and Dataflow to the level of "Serverless Data Analysis with Google BigQuery and Cloud Dataflow" on Coursera

    • Knowledge of Python and familiarity with the numpy package

    • Knowledge of undergraduate-level statistics to the level of a Basic Statistics course on Coursera

  • To be eligible for the free trial, you will need:

    - Google account (Google is currently blocked in China)

    - Credit card or bank account

    - Terms of service

    Note: There is a known issue with certain EU countries where individuals are not able to sign up, but you may sign up as "business" status and intend to see a potential economic benefit from the trial. More details at: https://support.google.com/cloud/answer/6090602

    More Google Cloud Platform free trial FAQs are available at: https://cloud.google.com/free-trial/

    For more details on how the free trial works, visit our documentation page: https://cloud.google.com/free-trial/docs/

  • If your current Google account is no longer eligible for the Google Cloud Platform free trial, you can create another Google account. Your new Google account should be used to sign up for the free trial.

  • View this page for more details: https://cloud.google.com/free-trial/docs/

  • Yes, this online course is based on the instructor-led training formerly known as CPB102.

  • The course covers the topics presented on the certification exam, however we recommend additional preparation including hands-on product experience. The best preparation for certification is real-world, hands-on experience. Review the Google Certified Professional Data Engineer certification preparation guide for further information and resources at https://cloud.google.com/certification/guides/data-engineer/

  • Google’s Certification Program gives customers and partners a way to demonstrate their technical skills in a particular job-role and technology. Individuals are assessed using a variety of rigorously developed industry-standard methods to determine whether they meet Google’s proficiency standards. Read more at https://cloud.google.com/certification/

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