About this Course

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 전문 분야의 5개 강좌 중 2번째 강좌:

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 20시간 필요

권장: 1 Woche Studium, 8–12 Stunden/Woche...

독일어

자막: 프랑스어, 포르투갈어 (브라질), 독일어, 영어, 스페인어, 일본어...

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 전문 분야의 5개 강좌 중 2번째 강좌:

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 20시간 필요

권장: 1 Woche Studium, 8–12 Stunden/Woche...

독일어

자막: 프랑스어, 포르투갈어 (브라질), 독일어, 영어, 스페인어, 일본어...

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 1시간 필요

Willkommen zum serverlosen maschinellen Lernen mit der Google Cloud Platform

2개 동영상 (총 5분), 1 reading, 1 quiz
2개의 동영상
Überlegungen zum maschinellen Lernen2m
1개의 읽기 자료
Kursressourcen herunterladen10m
1개 연습문제
ML-Kurs – Vorabfragen30m
완료하는 데 3시간 필요

Modul 1: Einführung in maschinelles Lernen

21개 동영상 (총 109분), 2 quizzes
21개의 동영상
Arten von ML3m
Die ML-Pipeline2m
Varianten des ML-Modells7m
ML-Problem eingrenzen2m
Maschinelles Lernen (ML) ausprobieren8m
Optimierung9m
Sichere Testumgebung für neuronale Netzwerke18m
Funktionen kombinieren3m
Feature Engineering3m
Bildmodelle5m
Effektives ML2m
Was macht ein gutes Dataset aus?5m
Fehlermesswerte3m
Genauigkeit2m
Genauigkeit und Trefferquote5m
Datasets für maschinelles Lernen erstellen3m
Datasets aufteilen6m
Python-Notebooks1m
Übersicht zum Lab "Datasets für maschinelles Lernen erstellen"3m
Zusammenfassung zum Lab "Datasets für maschinelles Lernen erstellen"2m
1개 연습문제
Quiz zu Modul 130m
완료하는 데 6시간 필요

Modul 2: ML-Modelle mit TensorFlow erstellen

15개 동영상 (총 65분), 5 quizzes
15개의 동영상
Was ist TensorFlow?5m
Core TensorFlow5m
Übersicht zum Lab "Einführung in TensorFlow"7
Zusammenfassung zum TensorFlow-Lab10m
Estimator API8m
Maschinelles Lernen mit tf.estimator15
Zusammenfassung zum Lab "Estimator"7m
Effektives ML ermöglichen6m
Einführung zum Lab "Refaktorierung zum Hinzufügen von Stapelverarbeitung und Funktionserstellung"38
Zusammenfassung zum Lab "Refaktorierung"4m
Trainieren und Bewerten4m
Monitoring1m
Einführung zum Lab "Verteiltes Training und Monitoring"2m
Zusammenfassung zum Lab "Verteiltes Training und Monitoring"7m
1개 연습문제
Quiz zu Modul 230m
완료하는 데 2시간 필요

Modul 3: ML-Modelle mit Cloud ML Engine skalieren

7개 동영상 (총 28분), 2 quizzes
7개의 동영상
Vorteile der Cloud ML Engine6m
Arbeitsablauf bei der Entwicklung1m
Trainingspakete erstellen3m
TensorFlow bereitstellen3m
Lab: ML hochskalieren39
Zusammenfassung zum Lab "ML hochskalieren"10m
1개 연습문제
Quiz für Modul 330m
완료하는 데 3시간 필요

Modul 4: Feature Engineering

16개 동영상 (총 92분), 2 quizzes
16개의 동영상
Gute Funktionen7m
Kausalität8m
Numerisch5m
Ausreichende Beispiele7m
Von den Rohdaten zur Funktion1m
Kategoriale Merkmale8m
Funktionsverknüpfungen3m
Bucketizing3m
Breit und tief5m
Einsatzbereiche für Feature Engineering3m
Überblick zum Lab "Feature Engineering"3m
Zusammenfassung zum Lab "Feature Engineering"10m
Hyperparameter-Abstimmung + Demo15m
ML-Abstraktionsebenen4m
Fazit1m
1개 연습문제
Quiz zu Modul 430m

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 auf Deutsch 전문 분야 정보

Dieser fünfwöchige Onlinevertiefungskurs bietet eine praktische Einführung zum Entwerfen und Erstellen von Datenverarbeitungssystemen auf der Google Cloud Platform. In Präsentationen, Demos und praxisorientierten Labs entwickeln die Teilnehmer Datenverarbeitungssysteme, erstellen End-to-End-Datenpipelines, analysieren Daten und üben maschinelles Lernen. Dieser Kurs vermittelt den Teilnehmern die folgenden Kompetenzen: • Datenverarbeitungssysteme auf der Google Cloud Platform entwickeln • Unstrukturierte Daten mit Spark und ML-APIs auf Cloud Dataproc verwenden • Batch- und Streaming-Daten durch die Implementierung von Autoscaling-Datenpipelines auf Cloud Dataflow verarbeiten • Mit Google BigQuery Geschäftsinformationen aus extrem großen Datasets ableiten • Modelle des maschinellen Lernens mit TensorFlow und Cloud ML trainieren, auswerten und damit Vorhersagen treffen • Sofortige Statistiken aus Streaming-Daten ermöglichen • Dieser Kurs richtet sich an erfahrene Entwickler, die für die Verwaltung von Big Data-Transformationen verantwortlich sind, zum Beispiel: • Daten extrahieren, laden, transformieren, bereinigen und validieren • Pipelines und Architekturen für die Datenverarbeitung entwerfen • Modelle des maschinellen Lernens und der Statistik erstellen und warten • Datasets abfragen, Abfrageergebnisse visualisieren und Berichte erstellen >>> Mit Ihrer Teilnahme an dieser Spezialisierung stimmen Sie den Nutzungsbedingungen von Qwiklabs zu, die Sie in den FAQs und unter folgendem Link finden: https://qwiklabs.com/terms_of_service <<<...
Data Engineering on Google Cloud Platform auf Deutsch

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/

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.