Acerca de este Curso
4.4
981 calificaciones
147 revisiones
This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: ● Identify use cases for machine learning ● Build an ML model using TensorFlow ● Build scalable, deployable ML models using Cloud ML ● Know the importance of preprocessing and combining features ● Incorporate advanced ML concepts into their models ● Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: ● Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience ● Basic proficiency with common query language such as SQL ● Experience with data modeling, extract, transform, load activities ● Developing applications using a common programming language such Python ● Familiarity with Machine Learning and/or statistics Google Account Notes: • Google services are currently unavailable in China....
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Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Calendar

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Intermediate Level

Nivel intermedio

Clock

Approx. 8 hours to complete

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

Subtítulos: English...

Habilidades que obtendrás

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud Computing
Globe

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Calendar

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Intermediate Level

Nivel intermedio

Clock

Approx. 8 hours to complete

Sugerido: 1 week of study, 8-12 hours/week...
Comment Dots

English

Subtítulos: English...

Programa - Qué aprenderás en este curso

Week
1
Clock
11 minutos para completar

Welcome to Serverless Machine Learning on Google Cloud Platform

...
Reading
2 videos (Total: 5 min), 1 quiz
Video2 videos
How to Think About Machine Learning2m
Quiz1 ejercicio de práctica
Machine Learning Course Pretest6m
Clock
3 horas para completar

Module 1: Getting Started with Machine Learning

...
Reading
21 videos (Total: 109 min), 2 quizzes
Video21 videos
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
Quiz1 ejercicio de práctica
Module 1 Quiz8m
Clock
5 horas para completar

Module 2: Building ML models with Tensorflow

...
Reading
15 videos (Total: 65 min), 5 quizzes
Video15 videos
What is TensorFlow ?5m
Core TensorFlow5m
Getting Started with TensorFlow Lab Overviewm
TensorFlow Lab Review10m
Estimator API8m
Machine Learning with tf.estimatorm
Estimator Lab Review7m
Building Effective ML6m
Lab Intro: Refactoring to add batching and feature creationm
Refactoring Lab Review4m
Train and Evaluate4m
Monitoring1m
Lab Intro: Distributed Training and Monitoring2m
Lab Review: Distributed Training and Monitoring7m
Quiz1 ejercicio de práctica
Module 2 Quiz8m
Clock
2 horas para completar

Module 3: Scaling ML models with Cloud ML Engine

...
Reading
7 videos (Total: 28 min), 2 quizzes
Video7 videos
Why Cloud ML Engine?6m
Development Workflow1m
Packaging trainer3m
TensorFlow Serving3m
Lab: Scaling up MLm
Lab Review: Scaling up ML10m
Quiz1 ejercicio de práctica
Module 3 Quiz4m
Clock
3 horas para completar

Module 4: Feature Engineering

...
Reading
16 videos (Total: 92 min), 2 quizzes
Video16 videos
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
Quiz1 ejercicio de práctica
Module 4 Quiz6m
4.4
Direction Signs

67%

comenzó una nueva carrera después de completar estos cursos
Briefcase

83%

consiguió un beneficio tangible en su carrera profesional gracias a este curso
Money

33%

consiguió un aumento de sueldo o ascenso

Principales revisiones

por 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.

por 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!

Acerca 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....

Acerca del programa especializado Data Engineering on Google Cloud Platform

>>> 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

Preguntas Frecuentes

  • Sí, puedes acceder a una vista preliminar del primer video y ver el programa antes de inscribirte. Debes comprar el curso para acceder a contenido que no está incluido en la vista preliminar

  • Si decides inscribirte en el curso antes de la fecha de inicio de la sesión, tendrás acceso a todos los videos y las lecturas de la lección para el curso. Podrás enviar tareas en cuanto comience la sesión.

  • Una vez que te inscribes y comienza la sesión, tendrás acceso a todos los videos y otros recursos, incluidos artículos de lectura y el foro de debate del curso. Podrás ver y enviar tareas de práctica y completar tareas con calificación obligatorias para obtener un título y un Certificado de curso

  • Si completas el curso de manera correcta, tu Certificado de curso electrónico se agregará a la página Logros. Desde allí, puedes imprimir tu Certificado de curso o agregarlo a tu perfil de LinkedIn

  • Este curso es uno de los pocos que se ofrecen en Coursera que está actualmente disponible solo para estudiantes que pagaron o que recibieron ayuda económica. / Si deseas tomar este curso, pero no puedes pagar la tarifa, te sugerimos enviar una solicitud de ayuda económica.

  • 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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