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中级

完成时间大约为11 小时

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

英语(English)

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您将获得的技能

TensorflowBigqueryGoogle Cloud PlatformCloud Computing

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为11 小时

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

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 3 小时

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 个视频 (总计 78 分钟), 2 个阅读材料, 2 个测验
13 个视频
Introduction to Google Cloud Platform3分钟
Compute Power for Analytic and ML Workloads9分钟
Demo: Creating a VM on Compute Engine13分钟
Elastic Storage with Google Cloud Storage5分钟
Build on Google's Global Network3分钟
Security: On-premise vs Cloud-native2分钟
Evolution of Google Cloud Big Data Tools5分钟
Getting Started with Google Cloud Platform and Qwiklabs3分钟
Choosing the right approach5分钟
What you can do with Google Cloud Platform3分钟
Activity: Explore real customer solution architectures7分钟
Key roles in a data-driven organization6分钟
2 个阅读材料
Google Cloud Public Datasets program10分钟
Module Resources10分钟
1 个练习
Module Review5分钟
完成时间为 2 小时

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 个视频 (总计 50 分钟), 1 个阅读材料, 2 个测验
8 个视频
Introduction to machine learning5分钟
Challenge: ML for recommending housing rentals8分钟
Approach: Move from on-premise to Google Cloud Platform9分钟
Demo: From zero to an Apache Spark job in 10 minutes or less6分钟
Challenge: Utilizing and tuning on-premise clusters6分钟
Move storage off-cluster with Google Cloud Storage4分钟
Lab Intro2分钟
1 个阅读材料
Module Resources5分钟
1 个练习
Module Review15分钟
完成时间为 3 小时

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 个视频 (总计 74 分钟), 2 个阅读材料, 2 个测验
13 个视频
Demo: Query 2 billion lines of Github code in less than 30 seconds11分钟
BigQuery: Fast SQL Engine4分钟
Demo: Exploring bike share data with SQL11分钟
Data quality4分钟
BigQuery managed storage5分钟
Insights from geographic data2分钟
Demo: Analyzing lightning strikes with BigQuery GIS7分钟
Choosing a ML model type for structured data4分钟
Predicting customer lifetime value5分钟
BigQueryML: Create models with SQL3分钟
Phases in ML model lifecycle2分钟
BigQuery ML: key features walkthrough5分钟
2 个阅读材料
Lab Intro10分钟
Module Resources10分钟
1 个练习
Module Review4分钟
2
完成时间为 2 小时

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 个视频 (总计 31 分钟), 1 个阅读材料, 2 个测验
8 个视频
Message-oriented architectures with Cloud Pub/Sub6分钟
Designing streaming pipelines with Apache Beam3分钟
Implementing streaming pipelines on Cloud Dataflow3分钟
Visualizing insights with Data Studio3分钟
Creating charts with Data Studio2分钟
Demo: Data Studio walkthrough7分钟
Lab Intro1分钟
1 个阅读材料
Module Resources10分钟
1 个练习
Module Review4分钟
完成时间为 2 小时

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 个视频 (总计 55 分钟), 2 个阅读材料, 2 个测验
10 个视频
How does ML on unstructured data work?3分钟
Demo: ML built into Google Photos1分钟
Comparing approaches to ML2分钟
Demo: Using ML building blocks7分钟
Using pre-built AI to create a chatbot4分钟
Customizing Pre-built models with AutoML7分钟
Lab Intro22
Building a Custom Model1分钟
Demo: Text classification done three ways21分钟
2 个阅读材料
Additional resources to build custom models10分钟
Module Resources10分钟
1 个练习
Module Review
完成时间为 5 分钟

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 个视频 (总计 5 分钟)
1 个视频
4.6
1,104 个审阅Chevron Right

46%

完成这些课程后已开始新的职业生涯

42%

通过此课程获得实实在在的工作福利

来自Google Cloud Platform Big Data and Machine Learning Fundamentals的热门评论

创建者 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.

创建者 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.

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关于 Data Engineering, Big Data, and Machine Learning on GCP 专项课程

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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