課程信息
4.4
1,068 個評分
157 個審閱
專項課程

第 4 門課程(共 5 門),位於

100% 在線

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

可靈活調整截止日期

根據您的日程表重置截止日期。
中級

中級

完成時間(小時)

完成時間大約為10 小時

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

英語(English)

字幕:英語(English)...

您將獲得的技能

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud Computing
專項課程

第 4 門課程(共 5 門),位於

100% 在線

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

可靈活調整截止日期

根據您的日程表重置截止日期。
中級

中級

完成時間(小時)

完成時間大約為10 小時

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

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(小時)
完成時間為 11 分鐘

Welcome to Serverless Machine Learning on Google Cloud Platform

...
Reading
2 個視頻(共 5 分鐘), 1 個測驗
Video2 個視頻
How to Think About Machine Learning2分鐘
Quiz1 個練習
Machine Learning Course Pretest6分鐘
完成時間(小時)
完成時間為 3 小時

Module 1: Getting Started with Machine Learning

...
Reading
21 個視頻(共 109 分鐘), 2 個測驗
Video21 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
Quiz1 個練習
Module 1 Quiz8分鐘
完成時間(小時)
完成時間為 5 小時

Module 2: Building ML models with Tensorflow

...
Reading
15 個視頻(共 65 分鐘), 5 個測驗
Video15 個視頻
What is TensorFlow ?5分鐘
Core TensorFlow5分鐘
Getting Started with TensorFlow Lab Overview分鐘
TensorFlow Lab Review10分鐘
Estimator API8分鐘
Machine Learning with tf.estimator分鐘
Estimator Lab Review7分鐘
Building Effective ML6分鐘
Lab Intro: Refactoring to add batching and feature creation分鐘
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
Quiz1 個練習
Module 2 Quiz8分鐘
完成時間(小時)
完成時間為 2 小時

Module 3: Scaling ML models with Cloud ML Engine

...
Reading
7 個視頻(共 28 分鐘), 2 個測驗
Video7 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 ML分鐘
Lab Review: Scaling up ML10分鐘
Quiz1 個練習
Module 3 Quiz4分鐘
完成時間(小時)
完成時間為 3 小時

Module 4: Feature Engineering

...
Reading
16 個視頻(共 92 分鐘), 2 個測驗
Video16 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
Quiz1 個練習
Module 4 Quiz6分鐘
4.4
職業方向

67%

完成這些課程後已開始新的職業生涯
工作福利

83%

通過此課程獲得實實在在的工作福利
職業晉升

33%

加薪或升職

熱門審閱

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 HMSep 8th 2018

A very good course on TensorFlow, ML and Google MLE on GCP.\n\nThe Labs are self contained and the problems proposed are very challenging. I learned a lot on this course.\n\nThank you!

關於 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....

關於 Data Engineering on Google Cloud Platform 專項課程

>>> 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 <<< ***COMPLETION CHALLENGE; receive a GCP t-shirt, see more below.*** This five-week specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. 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 COMPLETION CHALLENGE-Complete any GCP specialization from Oct 23 - Nov 30, 2018 to participate. Check Discussion Forums for full details....
Data Engineering on Google Cloud Platform

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。

  • 此课程是 Coursera 上提供的众多课程之一,当前只对已购买课程或已获得助学金的学生开放。如果您要学习此课程,但却承担不起课程费用,我们建议您提交助学金申请。

  • 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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