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中級

完成時間大約為11 小時

建議:1 week of study, 8-12 hours/week...

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您將獲得的技能

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud 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11 小時

建議:1 week of study, 8-12 hours/week...

英語(English)

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

Welcome to Serverless Machine Learning on Google Cloud Platform

...
2 個視頻 (總計 5 分鐘), 1 個測驗
2 個視頻
How to Think About Machine Learning2分鐘
1 個練習
Machine Learning Course Pretest6分鐘
完成時間為 3 小時

Module 1: Getting Started with Machine Learning

...
21 個視頻 (總計 109 分鐘), 1 個閱讀材料, 2 個測驗
21 個視頻
Types of ML3分鐘
The ML Pipeline2分鐘
Variants of ML model7分鐘
Framing a ML problem2分鐘
Playing with Machine Learning (ML)8分鐘
Optimization9分鐘
A Neural Network Playground18分鐘
Combining Features3分鐘
Feature Engineering3分鐘
Image Models5分鐘
Effective ML2分鐘
What makes a good dataset ?5分鐘
Error Metrics3分鐘
Accuracy2分鐘
Precision and Recall5分鐘
Creating Machine Learning Datasets3分鐘
Splitting Dataset6分鐘
Python Notebooks1分鐘
Create ML Datasets Lab Overview3分鐘
Create ML Datasets Lab Review2分鐘
1 個閱讀材料
About Machine Learning10分鐘
1 個練習
Module 1 Quiz8分鐘
完成時間為 5 小時

Module 2: Building ML models with Tensorflow

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15 個視頻 (總計 65 分鐘), 5 個測驗
15 個視頻
What is TensorFlow ?5分鐘
Core TensorFlow5分鐘
Getting Started with TensorFlow Lab Overview7
TensorFlow Lab Review10分鐘
Estimator API8分鐘
Machine Learning with tf.estimator15
Estimator Lab Review7分鐘
Building Effective ML6分鐘
Lab Intro: Refactoring to add batching and feature creation38
Refactoring Lab Review4分鐘
Train and Evaluate4分鐘
Monitoring1分鐘
Lab Intro: Distributed Training and Monitoring2分鐘
Lab Review: Distributed Training and Monitoring7分鐘
1 個練習
Module 2 Quiz8分鐘
完成時間為 2 小時

Module 3: Scaling ML models with Cloud ML Engine

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7 個視頻 (總計 28 分鐘), 1 個閱讀材料, 2 個測驗
7 個視頻
Why Cloud ML Engine?6分鐘
Development Workflow1分鐘
Packaging trainer3分鐘
TensorFlow Serving3分鐘
Lab: Scaling up ML39
Lab Review: Scaling up ML10分鐘
1 個閱讀材料
Kubeflow Pipelines10分鐘
1 個練習
Module 3 Quiz4分鐘
完成時間為 3 小時

Module 4: Feature Engineering

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16 個視頻 (總計 92 分鐘), 2 個閱讀材料, 2 個測驗
16 個視頻
Good Features7分鐘
Causality8分鐘
Numeric5分鐘
Enough Examples7分鐘
Raw Data to Features1分鐘
Categorical Features8分鐘
Feature Crosses3分鐘
Bucketizing3分鐘
Wide and Deep5分鐘
Where to do Feature Engineering3分鐘
Feature Engineering Lab Overview3分鐘
Feature Engineering Lab Review10分鐘
Hyperparameter Tuning + Demo15分鐘
ML Abstraction Levels4分鐘
Summary1分鐘
2 個閱讀材料
ML APIs and Cloud AutoML10分鐘
BigQuery ML10分鐘
1 個練習
Module 4 Quiz6分鐘
4.4
231 個審閱Chevron Right

50%

完成這些課程後已開始新的職業生涯

44%

通過此課程獲得實實在在的工作福利

15%

加薪或升職

來自Serverless Machine Learning with Tensorflow on Google Cloud Platform的熱門評論

創建者 NPJan 9th 2018

Thank you very much for making this course available on Coursera, I cannot agree more the knowledge of Mr Venkat. This is a great way to help people to get started with Google Machine Learning.

創建者 MGSep 21st 2017

Great course! I've learnt a lot. The concepts where super clear. The coding part was a little difficult, I didn't understand all af it, but it's good to have a complete example to use.

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

常見問題

  • 是的,您可以在注册之前预览第一个视频和查看授课大纲。您必须购买课程,才能访问预览不包括的内容。

  • 如果您决定在班次开始日期之前注册课程,那么您将可以访问课程的所有课程视频和阅读材料。班次开始之后,您便可以提交作业。

  • 在您注册且班次开课之后,您将可以访问所有视频和其他资源,包括阅读材料内容和课程论坛。您将能够查看和提交练习作业,并完成所需的评分作业以获得成绩和课程证书。

  • 如果您成功完成课程,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。

  • 该课程是 Coursera 上提供的为数不多的课程之一,目前只对已购买课程或已获得助学金的学生开放。

  • 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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