このコースについて
126,034 最近の表示

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約11時間で修了

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

英語

字幕:フランス語, ポルトガル語(ブラジル), ドイツ語, 英語, スペイン語, 日本語...

習得するスキル

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud Computing

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約11時間で修了

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

英語

字幕:フランス語, ポルトガル語(ブラジル), ドイツ語, 英語, スペイン語, 日本語...

シラバス - 本コースの学習内容

1
11分で修了

Welcome to Serverless Machine Learning on Google Cloud Platform

...
2件のビデオ (合計5分), 1 quiz
1の練習問題
Machine Learning Course Pretest6 分
3時間で修了

Module 1: Getting Started with Machine Learning

...
21件のビデオ (合計109分), 1 reading, 2 quizzes
21件のビデオ
Variants of ML model7 分
Framing a ML problem2 分
Playing with Machine Learning (ML)8 分
Optimization9 分
A Neural Network Playground18 分
Combining Features3 分
Feature Engineering3 分
Image Models5 分
Effective ML2 分
What makes a good dataset ?5 分
Error Metrics3 分
Accuracy2 分
Precision and Recall5 分
Creating Machine Learning Datasets3 分
Splitting Dataset6 分
Python Notebooks1 分
Create ML Datasets Lab Overview3 分
Create ML Datasets Lab Review2 分
1件の学習用教材
About Machine Learning10 分
1の練習問題
Module 1 Quiz8 分
5時間で修了

Module 2: Building ML models with Tensorflow

...
15件のビデオ (合計65分), 5 quizzes
15件のビデオ
Getting Started with TensorFlow Lab Overview7
TensorFlow Lab Review10 分
Estimator API8 分
Machine Learning with tf.estimator15
Estimator Lab Review7 分
Building Effective ML6 分
Lab Intro: Refactoring to add batching and feature creation38
Refactoring Lab Review4 分
Train and Evaluate4 分
Monitoring1 分
Lab Intro: Distributed Training and Monitoring2 分
Lab Review: Distributed Training and Monitoring7 分
1の練習問題
Module 2 Quiz8 分
2時間で修了

Module 3: Scaling ML models with Cloud ML Engine

...
7件のビデオ (合計28分), 1 reading, 2 quizzes
7件のビデオ
Packaging trainer3 分
TensorFlow Serving3 分
Lab: Scaling up ML39
Lab Review: Scaling up ML10 分
1件の学習用教材
Kubeflow Pipelines10 分
1の練習問題
Module 3 Quiz4 分
3時間で修了

Module 4: Feature Engineering

...
16件のビデオ (合計92分), 2 readings, 2 quizzes
16件のビデオ
Numeric5 分
Enough Examples7 分
Raw Data to Features1 分
Categorical Features8 分
Feature Crosses3 分
Bucketizing3 分
Wide and Deep5 分
Where to do Feature Engineering3 分
Feature Engineering Lab Overview3 分
Feature Engineering Lab Review10 分
Hyperparameter Tuning + Demo15 分
ML Abstraction Levels4 分
Summary1 分
2件の学習用教材
ML APIs and Cloud AutoML10 分
BigQuery ML10 分
1の練習問題
Module 4 Quiz6 分
4.4
231件のレビューChevron Right

50%

コース終了後に新しいキャリアをスタートした

44%

コースが具体的なキャリアアップにつながった

15%

昇給や昇進につながった

Serverless Machine Learning with Tensorflow on Google Cloud Platform からの人気レビュー

by 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.

by 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.

Google Cloudについて

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, Big Data, and Machine Learning on GCPの専門講座について

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

よくある質問

  • はい、最初のビデオをプレビューしてシラバスを表示してから登録できます。プレビューに含まれないコンテンツにアクセスするには、コースを購入する必要があります。

  • セッションの開始日前にコースに登録すると、そのコースに関するすべての講座のビデオと学習用教材にアクセスできます。課題は、セッションの開始後に提出できるようになります。

  • 登録してセッションを開始すると、すべてのビデオや、学習用教材項目やコースのディスカッションフォーラムなど他のリソースにアクセスできます。演習の評価を表示して提出したり、成績とコース修了証の取得に必要なテストを完了することができます。

  • コースを無事完了すると、コースの電子修了証が成果のページに追加されます。そこからコースの修了証を印刷したり、LinkedInのプロフィールに追加したりできます。

  • このコースは現在、利用できる期間内において、支払い済み受講生または学資援助を受けた受講生のみが利用できるCoursera(コーセラ)提供のコースです。

  • 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/

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。