Este curso intensivo sob demanda de quatro dias oferece aos participantes uma introdução sobre como projetar e criar sistemas de machine learning no Google Cloud Platform. Por meio de apresentações, demonstrações e laboratórios práticos, os participantes aprenderão os conceitos de machine learning (ML) e do TensorFlow, além de habilidades de desenvolvimento, avaliação e produção de modelos de ML.
This course is part of the Data Engineering on Google Cloud Platform em Português Brasileiro Specialization
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Course 2 of 5 in the
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Intermediate Level
Approx. 14 hours to complete
Portuguese (Brazilian)
Subtitles: French, Portuguese (Brazilian), German, English, Spanish, Japanese
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 14 hours to complete
Portuguese (Brazilian)
Subtitles: French, Portuguese (Brazilian), German, English, Spanish, Japanese
Offered by
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.
Syllabus - What you will learn from this course
1 hour to complete
Este é o "Serverless Machine Learning on Google Cloud Platform"
1 hour to complete
2 videos (Total 5 min)
1 practice exercise
Pré-teste do curso de machine learning30m
3 hours to complete
Módulo 1: Primeiros passos com machine learning
3 hours to complete
21 videos (Total 109 min)
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 130m
6 hours to complete
Módulo 2: Criação de modelos de ML com o TensorFlow
6 hours to complete
15 videos (Total 65 min)
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 230m
2 hours to complete
Módulo 3: Escalonamento de modelos de ML com o Cloud ML Engine
2 hours to complete
7 videos (Total 28 min)
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 330m
3 hours to complete
Módulo 4: Engenharia de atributos
3 hours to complete
16 videos (Total 92 min)
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 430m
About the Data Engineering on Google Cloud Platform em Português Brasileiro 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 <<<
Frequently Asked Questions
Can I preview a course before enrolling?
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.
When will I have access to the lectures and assignments?
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.
What will I get when I enroll?
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.
When will I receive my 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.
Why can’t I audit this course?
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.
What is the refund policy?
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Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Are there prerequisites for this course?
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
Am I eligible for the Google Cloud Platform free trial?
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/
What if I have already used up my Google Cloud Platform free trial?
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.
How does the free trial work?
View this page for more details: https://cloud.google.com/free-trial/docs/
Is this course similar to CPB102?
Yes, this online course is based on the instructor-led training formerly known as CPB102.
Does this course relate to the Google Certified Professional Data Engineer certification?
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/
What is a Google certification?
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.
