About this Course
89,127

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 11시간 필요

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

영어

자막: 영어

귀하가 습득할 기술

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud Computing

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 11시간 필요

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

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 11분 필요

Welcome to Serverless Machine Learning on Google Cloud Platform

...
2 videos (Total 5 min), 1 quiz
2개의 동영상
How to Think About Machine Learning2m
1개 연습문제
Machine Learning Course Pretest6m
완료하는 데 3시간 필요

Module 1: Getting Started with Machine Learning

...
21 videos (Total 109 min), 1 reading, 2 quizzes
21개의 동영상
Types of ML3m
The ML Pipeline2m
Variants of ML model7m
Framing a ML problem2m
Playing with Machine Learning (ML)8m
Optimization9m
A Neural Network Playground18m
Combining Features3m
Feature Engineering3m
Image Models5m
Effective ML2m
What makes a good dataset ?5m
Error Metrics3m
Accuracy2m
Precision and Recall5m
Creating Machine Learning Datasets3m
Splitting Dataset6m
Python Notebooks1m
Create ML Datasets Lab Overview3m
Create ML Datasets Lab Review2m
1개의 읽기 자료
About Machine Learning10m
1개 연습문제
Module 1 Quiz8m
완료하는 데 5시간 필요

Module 2: Building ML models with Tensorflow

...
15 videos (Total 65 min), 5 quizzes
15개의 동영상
What is TensorFlow ?5m
Core TensorFlow5m
Getting Started with TensorFlow Lab Overview7
TensorFlow Lab Review10m
Estimator API8m
Machine Learning with tf.estimator15
Estimator Lab Review7m
Building Effective ML6m
Lab Intro: Refactoring to add batching and feature creation38
Refactoring Lab Review4m
Train and Evaluate4m
Monitoring1m
Lab Intro: Distributed Training and Monitoring2m
Lab Review: Distributed Training and Monitoring7m
1개 연습문제
Module 2 Quiz8m
완료하는 데 2시간 필요

Module 3: Scaling ML models with Cloud ML Engine

...
7 videos (Total 28 min), 1 reading, 2 quizzes
7개의 동영상
Why Cloud ML Engine?6m
Development Workflow1m
Packaging trainer3m
TensorFlow Serving3m
Lab: Scaling up ML39
Lab Review: Scaling up ML10m
1개의 읽기 자료
Kubeflow Pipelines10m
1개 연습문제
Module 3 Quiz4m
완료하는 데 3시간 필요

Module 4: Feature Engineering

...
16 videos (Total 92 min), 2 readings, 2 quizzes
16개의 동영상
Good Features7m
Causality8m
Numeric5m
Enough Examples7m
Raw Data to Features1m
Categorical Features8m
Feature Crosses3m
Bucketizing3m
Wide and Deep5m
Where to do Feature Engineering3m
Feature Engineering Lab Overview3m
Feature Engineering Lab Review10m
Hyperparameter Tuning + Demo15m
ML Abstraction Levels4m
Summary1m
2개의 읽기 자료
ML APIs and Cloud AutoML10m
BigQuery ML10m
1개 연습문제
Module 4 Quiz6m
4.4
223개의 리뷰Chevron Right

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이 강좌를 수료한 후 새로운 경력 시작하기

44%

이 강좌를 통해 확실한 경력상 이점 얻기

15%

급여 인상 또는 승진하기

최상위 리뷰

대학: 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.

대학: 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!

Google 클라우드 정보

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....

Data Engineering on Google Cloud Platform 전문 분야 정보

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 on Google Cloud Platform

자주 묻는 질문

  • 예. 등록하기 전에 첫 번째 비디오를 미리 보고 강의 계획을 검토할 수 있습니다. 미리 보기에 포함되지 않은 콘텐츠를 이용하려면 강좌를 구매해야 합니다.

  • 세션 시작일 전에 강좌에 등록하면 해당 강좌의 모든 강의 비디오 및 읽기 자료에 접근할 수 있습니다. 수업이 시작되면 과제를 제출할 수 있습니다.

  • 등록 후 세션이 시작되면 읽기 자료 항목 및 강좌 토론 포럼을 포함하여 모든 비디오와 기타 리소스를 이용할 수 있습니다. 연습 평가를 보고 제출하며 필요한 성적 평가 과제를 완료하여 성적을 받고 강좌 수료증을 취득할 수 있습니다.

  • 강좌를 성공적으로 수료하면 전자 강좌 수료증이 성취도 페이지에 추가됩니다. 해당 페이지에서 강좌 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다.

  • 이 강좌는 현재 Coursera에서 수업료를 결제했거나 재정 지원(해당하는 경우)을 받은 학습자만 이용할 수 있는 강좌입니다.

  • 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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