课程信息
4.4
1,158 个评分
166 个审阅
100% 在线

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

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

完成时间(小时)

完成时间大约为10 小时

建议:1 week of study, 8-12 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud Computing
100% 在线

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

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

完成时间(小时)

完成时间大约为10 小时

建议:1 week of study, 8-12 hours/week...
可选语言

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(小时)
完成时间为 11 分钟

Welcome to Serverless Machine Learning on Google Cloud Platform

...
Reading
2 个视频 (总计 5 分钟), 1 个测验
Video2 个视频
How to Think About Machine Learning2分钟
Quiz1 个练习
Machine Learning Course Pretest6分钟
完成时间(小时)
完成时间为 3 小时

Module 1: Getting Started with Machine Learning

...
Reading
21 个视频 (总计 109 分钟), 2 个测验
Video21 个视频
Types of ML3分钟
The ML Pipeline2分钟
Variants of ML model7分钟
Framing a ML problem2分钟
Playing with Machine Learning (ML)8分钟
Optimization9分钟
A Neural Network Playground18分钟
Combining Features3分钟
Feature Engineering3分钟
Image Models5分钟
Effective ML2分钟
What makes a good dataset ?5分钟
Error Metrics3分钟
Accuracy2分钟
Precision and Recall5分钟
Creating Machine Learning Datasets3分钟
Splitting Dataset6分钟
Python Notebooks1分钟
Create ML Datasets Lab Overview3分钟
Create ML Datasets Lab Review2分钟
Quiz1 个练习
Module 1 Quiz8分钟
完成时间(小时)
完成时间为 5 小时

Module 2: Building ML models with Tensorflow

...
Reading
15 个视频 (总计 65 分钟), 5 个测验
Video15 个视频
What is TensorFlow ?5分钟
Core TensorFlow5分钟
Getting Started with TensorFlow Lab Overview分钟
TensorFlow Lab Review10分钟
Estimator API8分钟
Machine Learning with tf.estimator分钟
Estimator Lab Review7分钟
Building Effective ML6分钟
Lab Intro: Refactoring to add batching and feature creation分钟
Refactoring Lab Review4分钟
Train and Evaluate4分钟
Monitoring1分钟
Lab Intro: Distributed Training and Monitoring2分钟
Lab Review: Distributed Training and Monitoring7分钟
Quiz1 个练习
Module 2 Quiz8分钟
完成时间(小时)
完成时间为 2 小时

Module 3: Scaling ML models with Cloud ML Engine

...
Reading
7 个视频 (总计 28 分钟), 2 个测验
Video7 个视频
Why Cloud ML Engine?6分钟
Development Workflow1分钟
Packaging trainer3分钟
TensorFlow Serving3分钟
Lab: Scaling up ML分钟
Lab Review: Scaling up ML10分钟
Quiz1 个练习
Module 3 Quiz4分钟
完成时间(小时)
完成时间为 3 小时

Module 4: Feature Engineering

...
Reading
16 个视频 (总计 92 分钟), 2 个测验
Video16 个视频
Good Features7分钟
Causality8分钟
Numeric5分钟
Enough Examples7分钟
Raw Data to Features1分钟
Categorical Features8分钟
Feature Crosses3分钟
Bucketizing3分钟
Wide and Deep5分钟
Where to do Feature Engineering3分钟
Feature Engineering Lab Overview3分钟
Feature Engineering Lab Review10分钟
Hyperparameter Tuning + Demo15分钟
ML Abstraction Levels4分钟
Summary1分钟
Quiz1 个练习
Module 4 Quiz6分钟
4.4
166 个审阅Chevron Right
职业方向

67%

完成这些课程后已开始新的职业生涯
工作福利

83%

通过此课程获得实实在在的工作福利
职业晋升

33%

加薪或升职

热门审阅

创建者 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.

创建者 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!

关于 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....

关于 Data Engineering on Google Cloud Platform 专项课程

>>> 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 <<< 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...
Data Engineering on Google Cloud Platform

常见问题

  • 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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