About this Course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 9 hours to complete

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

Portuguese (Brazilian)

Subtitles: French, Portuguese (Brazilian), German, English, Spanish, Japanese...

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 9 hours to complete

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

Portuguese (Brazilian)

Subtitles: French, Portuguese (Brazilian), German, English, Spanish, Japanese...

Syllabus - What you will learn from this course

Week
1
11 minutes to complete

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

2 videos (Total 5 min), 1 quiz
2 videos
Considerações sobre machine learning2m
1 practice exercise
Pré-teste do curso de machine learning6m
3 hours to complete

Módulo 1: Primeiros passos com machine learning

21 videos (Total 109 min), 2 quizzes
21 videos
Tipos de ML3m
O canal de ML2m
Variantes do modelo de ML7m
Como classificar um problema de ML2m
Como usar machine learning (ML)8m
Otimização9m
Um playground de rede neural18m
Como combinar atributos3m
Engenharia de atributos3m
Modelos de imagem5m
ML eficaz2m
Quais são os elementos de um bom conjunto de dados?5m
Métricas de erro3m
Precisão2m
Precisão e recall5m
Como criar conjuntos de dados de machine learning3m
Como dividir conjuntos de dados6m
Python Notebooks1m
Visão geral do laboratório Como criar conjuntos de dados de ML3m
Revisão do laboratório Como criar conjuntos de dados de ML2m
1 practice exercise
Teste do módulo 18m
5 hours to complete

Módulo 2: Criação de modelos de ML com o TensorFlow

15 videos (Total 65 min), 5 quizzes
15 videos
O que é o TensorFlow?5m
Principais características do TensorFlow5m
Visão geral do laboratório Primeiros passos com o TensorFlow7s
Revisão do laboratório TensorFlow10m
API Estimator8m
Machine learning com o tf.estimator15s
Revisão do laboratório Estimator7m
Como criar ML eficaz6m
Introdução ao laboratório Refatoração para adicionar a criação de lotes e recursos38s
Revisão do laboratório Refatoração4m
Treine e avalie4m
Monitoramento1m
Introdução ao laboratório: Treinamento e monitoramento distribuídos2m
Revisão do laboratório: Treinamento e monitoramento distribuídos7m
1 practice exercise
Teste do módulo 28m
2 hours to complete

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

7 videos (Total 28 min), 2 quizzes
7 videos
Por que usar o Cloud ML Engine?6m
Fluxo de trabalho de desenvolvimento1m
Como empacotar o treinador3m
TensorFlow Serving3m
Laboratório: Como escalonar ML39s
Revisão do laboratório: Como escalonar ML10m
1 practice exercise
Teste do módulo 34m
3 hours to complete

Módulo 4: Engenharia de atributos

16 videos (Total 92 min), 2 quizzes
16 videos
Atributos bons7m
Causalidade8m
Numérico5m
Exemplos suficientes7m
Dados brutos para os atributos1m
Atributos categóricos8m
Cruzamento de atributos3m
Como criar intervalos3m
Amplitude e profundidade5m
Onde aplicar a engenharia de atributos3m
Visão geral do laboratório Engenharia de atributos3m
Revisão do laboratório Engenharia de atributos10m
Ajuste de hiperparâmetro e demonstração15m
Níveis de abstração de ML4m
Resumo1m
1 practice exercise
Teste do módulo 46m

About Google Cloud

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....

About the Data Engineering on Google Cloud Platform em Português Specialization

Nesta especialização on-line intensiva de cinco semanas, os participantes terão uma introdução prática sobre como projetar e criar sistemas de processamento de dados no Google Cloud Platform. Por meio de uma combinação de apresentações, demonstrações e laboratórios práticos, os participantes aprenderão a projetar sistemas de processamento de dados, criar canais completos e análises de dados e desenvolver soluções de aprendizado de máquina. Neste curso, abordamos dados estruturados, não estruturados e de streaming. Neste curso, os participantes irão adquirir as seguintes habilidades: • projetar e criar sistemas de processamento de dados no Google Cloud Platform • usar dados não estruturados com as APIs do Spark e de aprendizado de máquina no Cloud Dataproc • processar dados em lote e streaming com a implementação de canais de dados de escalonamento automático no Cloud Dataflow • derivar insights de negócios a partir de conjuntos de dados extremamente grandes usando o Google BigQuery • treinar, avaliar e prever com modelos de aprendizado de máquina usando o TensorFlow e o Cloud ML • ativar insights instantâneos dos dados de streaming Esta aula destina-se a desenvolvedores experientes responsáveis pelo gerenciamento de transformações de Big Data. >>> Ao se inscrever nesta especialização, você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<...
Data Engineering on Google Cloud Platform em Português

Frequently Asked Questions

  • Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

  • If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

  • Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

  • If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

  • This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

  • 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/

More questions? Visit the Learner Help Center.