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完成時間大約為12 小時

建議:1 week of study, 6-10 hours/week...

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您將獲得的技能

TensorflowBigqueryGoogle Cloud PlatformCloud Computing

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12 小時

建議:1 week of study, 6-10 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 3 小時

Introduction to the Data and Machine Learning on Google Cloud Platform Specialization

Welcome to the Big Data and Machine Learning fundamentals on GCP course. Here you will learn the basics of how the course is structured and the four main big data challenges you will solve for.

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13 個視頻 (總計 78 分鐘), 2 個閱讀材料, 2 個測驗
13 個視頻
Demo: Creating a VM on Compute Engine13分鐘
Elastic Storage with Google Cloud Storage5分鐘
Build on Google's Global Network3分鐘
Security: On-premise vs Cloud-native2分鐘
Evolution of Google Cloud Big Data Tools5分鐘
Getting Started with Google Cloud Platform and Qwiklabs3分鐘
Choosing the right approach5分鐘
What you can do with Google Cloud Platform3分鐘
Activity: Explore real customer solution architectures7分鐘
Key roles in a data-driven organization6分鐘
2 個閱讀材料
Google Cloud Public Datasets program10分鐘
Module Resources10分鐘
1 個練習
Module Review5分鐘
完成時間為 2 小時

Recommending Products using Cloud SQL and Spark

In this module you will have an existing Apache SparkML recommendation model that is running on-premise. You will learn about recommendation models and how you can run them in the cloud with Cloud Dataproc and Cloud SQL.

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8 個視頻 (總計 50 分鐘), 1 個閱讀材料, 2 個測驗
8 個視頻
Approach: Move from on-premise to Google Cloud Platform9分鐘
Demo: From zero to an Apache Spark job in 10 minutes or less6分鐘
Challenge: Utilizing and tuning on-premise clusters6分鐘
Move storage off-cluster with Google Cloud Storage4分鐘
Lab Intro2分鐘
1 個閱讀材料
Module Resources5分鐘
1 個練習
Module Review15分鐘
完成時間為 3 小時

Predict Visitor Purchases with BigQuery ML

In this module, you will learn the foundations of BigQuery and big data analysis at scale. You will then learn how to build your own custom machine learning model to predict visitor purchases using just SQL with BigQuery ML.

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13 個視頻 (總計 74 分鐘), 2 個閱讀材料, 2 個測驗
13 個視頻
Demo: Exploring bike share data with SQL11分鐘
Data quality4分鐘
BigQuery managed storage5分鐘
Insights from geographic data2分鐘
Demo: Analyzing lightning strikes with BigQuery GIS7分鐘
Choosing a ML model type for structured data4分鐘
Predicting customer lifetime value5分鐘
BigQueryML: Create models with SQL3分鐘
Phases in ML model lifecycle2分鐘
BigQuery ML: key features walkthrough5分鐘
2 個閱讀材料
Lab Intro10分鐘
Module Resources10分鐘
1 個練習
Module Review4分鐘
2
完成時間為 2 小時

Create Streaming Data Pipelines with Cloud Pub/sub and Cloud Dataflow

In this module you will engineer and build an auto-scaling streaming data pipeline to ingest, process, and visualize data on a dashboard. Before you build your pipeline you'll learn the foundations of message-oriented architecture and pitfalls to avoid when designing and implementing modern data pipelines.

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8 個視頻 (總計 31 分鐘), 1 個閱讀材料, 2 個測驗
8 個視頻
Implementing streaming pipelines on Cloud Dataflow3分鐘
Visualizing insights with Data Studio3分鐘
Creating charts with Data Studio2分鐘
Demo: Data Studio walkthrough7分鐘
Lab Intro1分鐘
1 個閱讀材料
Module Resources10分鐘
1 個練習
Module Review4分鐘
完成時間為 2 小時

Classify Images with Pre-Built Models using Vision API and Cloud AutoML

Don't want to create a custom ML model from scratch? Learn how to leverage and extend pre-built ML models like the Vision API and Cloud AutoML for image classification.

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10 個視頻 (總計 55 分鐘), 2 個閱讀材料, 2 個測驗
10 個視頻
Comparing approaches to ML2分鐘
Demo: Using ML building blocks7分鐘
Using pre-built AI to create a chatbot4分鐘
Customizing Pre-built models with AutoML7分鐘
Lab Intro22
Building a Custom Model1分鐘
Demo: Text classification done three ways21分鐘
2 個閱讀材料
Additional resources to build custom models10分鐘
Module Resources10分鐘
1 個練習
Module Review
完成時間為 5 分鐘

Summary

In this final module, we will review the key challenges, solutions, and topics covered as part of this fundamentals course. We will also review additional resources and the steps you can take to get certified as a Google Cloud Data Engineer.

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1 個視頻 (總計 5 分鐘)
1 個視頻
4.6
1173 個審閱Chevron Right

46%

完成這些課程後已開始新的職業生涯

42%

通過此課程獲得實實在在的工作福利

來自Google Cloud Platform Big Data and Machine Learning Fundamentals的熱門評論

創建者 VSMar 3rd 2019

Overall a good curated course to help understand the GCP offerings and high level architecture of how their offerings fit in the current landscape. Easy to follow along as this was fundamental course.

創建者 CRDec 27th 2017

This was a great course to understand at a high level how to design and create my data ecosystem and how to do it sustainably. Hopefully, next courses provide more in-depth the technical features.

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....

關於 Data Engineering, Big Data, and Machine Learning on GCP 專項課程

This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. This course teaches the following skills: • Design and build data processing systems on Google Cloud Platform • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow • Derive business insights from extremely large datasets using Google BigQuery • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML • Enable instant insights from streaming data This class is intended for developers who are responsible for: • Extracting, Loading, Transforming, cleaning, and validating data • Designing pipelines and architectures for data processing • Creating and maintaining machine learning and statistical models • Querying datasets, visualizing query results and creating reports >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<...
Data Engineering, Big Data, and Machine Learning on GCP

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

    • A common query language such as SQL

    • Extract, transform, load activities

    • Data modeling

    • Machine learning and/or statistics

    • Programming in Python

  • 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 CPB100.

  • 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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