À propos de ce cours
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Dates limites flexibles

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Niveau intermédiaire

Approx. 12 heures pour terminer

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


Sous-titres : Français, Portugais (brésilien), Allemand, Anglais, Espagnol, Japonais...

Compétences que vous acquerrez

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud Computing

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

Approx. 12 heures pour terminer

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


Sous-titres : Français, Portugais (brésilien), Allemand, Anglais, Espagnol, Japonais...

Programme du cours : ce que vous apprendrez dans ce cours

11 minutes pour terminer

Welcome to Serverless Machine Learning on Google Cloud Platform

2 vidéos (Total 5 min), 1 quiz
1 exercice pour s'entraîner
Machine Learning Course Pretest6 min
3 heures pour terminer

Module 1: Getting Started with Machine Learning

21 vidéos (Total 109 min), 1 lecture, 2 quiz
21 vidéos
Variants of ML model7 min
Framing a ML problem2 min
Playing with Machine Learning (ML)8 min
Optimization9 min
A Neural Network Playground18 min
Combining Features3 min
Feature Engineering3 min
Image Models5 min
Effective ML2 min
What makes a good dataset ?5 min
Error Metrics3 min
Accuracy2 min
Precision and Recall5 min
Creating Machine Learning Datasets3 min
Splitting Dataset6 min
Python Notebooks1 min
Create ML Datasets Lab Overview3 min
Create ML Datasets Lab Review2 min
1 lecture
About Machine Learning10 min
1 exercice pour s'entraîner
Module 1 Quiz8 min
5 heures pour terminer

Module 2: Building ML models with Tensorflow

15 vidéos (Total 65 min), 5 quiz
15 vidéos
Getting Started with TensorFlow Lab Overview7s
TensorFlow Lab Review10 min
Estimator API8 min
Machine Learning with tf.estimator15s
Estimator Lab Review7 min
Building Effective ML6 min
Lab Intro: Refactoring to add batching and feature creation38s
Refactoring Lab Review4 min
Train and Evaluate4 min
Monitoring1 min
Lab Intro: Distributed Training and Monitoring2 min
Lab Review: Distributed Training and Monitoring7 min
1 exercice pour s'entraîner
Module 2 Quiz8 min
2 heures pour terminer

Module 3: Scaling ML models with Cloud ML Engine

7 vidéos (Total 28 min), 1 lecture, 2 quiz
7 vidéos
Packaging trainer3 min
TensorFlow Serving3 min
Lab: Scaling up ML39s
Lab Review: Scaling up ML10 min
1 lecture
Kubeflow Pipelines10 min
1 exercice pour s'entraîner
Module 3 Quiz4 min
3 heures pour terminer

Module 4: Feature Engineering

16 vidéos (Total 92 min), 2 lectures, 2 quiz
16 vidéos
Numeric5 min
Enough Examples7 min
Raw Data to Features1 min
Categorical Features8 min
Feature Crosses3 min
Bucketizing3 min
Wide and Deep5 min
Where to do Feature Engineering3 min
Feature Engineering Lab Overview3 min
Feature Engineering Lab Review10 min
Hyperparameter Tuning + Demo15 min
ML Abstraction Levels4 min
Summary1 min
2 lectures
ML APIs and Cloud AutoML10 min
BigQuery ML10 min
1 exercice pour s'entraîner
Module 4 Quiz6 min
261 avisChevron Right


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Principaux examens pour Serverless Machine Learning with Tensorflow on Google Cloud Platform

par 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.

par 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!

À propos de 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....

À propos du Spécialisation Data Engineering, Big Data, and Machine Learning on GCP

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

Foire Aux Questions

  • Oui, vous pouvez prévisualiser la première vidéo et consulter le programme du cours avant de vous inscrire. Vous devez acheter le cours pour accéder au contenu non inclus dans la prévisualisation.

  • Si vous décidez de vous inscrire au cours avant la date de début de session, vous aurez accès à toutes les vidéos et lectures du cours. Vous pourrez soumettre des devoirs à partir du début de la session.

  • Une fois que vous êtes inscrit(e) et que votre session commence, vous avez accès à toutes les vidéos et aux autres ressources, y compris les éléments à lire et le forum de discussion du cours. Vous pouvez afficher et soumettre des devoirs pour vous exercer, et terminer les devoirs notés requis pour obtenir une note et un Certificat de Cours.

  • Si vous réussissez le cours, votre Certificat de Cours électronique sera ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat de Cours ou l'ajouter à votre profil LinkedIn.

  • Ce cours fait partie du nombre restreint de cours proposés par Coursera actuellement disponibles uniquement aux étudiants ayant payé les frais du cours ou bénéficié de l'Aide Financière, lorsqu'elle est disponible.

  • 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/

D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.