Informações sobre o curso
4.4
977 classificações
147 avaliações
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% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Intermediate Level

Nível intermediário

Clock

Approx. 8 hours to complete

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

English

Legendas: English...

Habilidades que você terá

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud Computing
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Intermediate Level

Nível intermediário

Clock

Approx. 8 hours to complete

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

English

Legendas: English...

Programa - O que você aprenderá com este curso

Week
1
Clock
11 minutos para concluir

Welcome to Serverless Machine Learning on Google Cloud Platform

...
Reading
2 vídeos (Total de 5 min), 1 teste
Video2 videos
How to Think About Machine Learning2min
Quiz1 exercício prático
Machine Learning Course Pretest6min
Clock
3 horas para concluir

Module 1: Getting Started with Machine Learning

...
Reading
21 vídeos (Total de 109 min), 2 testes
Video21 videos
Types of ML3min
The ML Pipeline2min
Variants of ML model7min
Framing a ML problem2min
Playing with Machine Learning (ML)8min
Optimization9min
A Neural Network Playground18min
Combining Features3min
Feature Engineering3min
Image Models5min
Effective ML2min
What makes a good dataset ?5min
Error Metrics3min
Accuracy2min
Precision and Recall5min
Creating Machine Learning Datasets3min
Splitting Dataset6min
Python Notebooks1min
Create ML Datasets Lab Overview3min
Create ML Datasets Lab Review2min
Quiz1 exercício prático
Module 1 Quiz8min
Clock
5 horas para concluir

Module 2: Building ML models with Tensorflow

...
Reading
15 vídeos (Total de 65 min), 5 testes
Video15 videos
What is TensorFlow ?5min
Core TensorFlow5min
Getting Started with TensorFlow Lab Overviewmin
TensorFlow Lab Review10min
Estimator API8min
Machine Learning with tf.estimatormin
Estimator Lab Review7min
Building Effective ML6min
Lab Intro: Refactoring to add batching and feature creationmin
Refactoring Lab Review4min
Train and Evaluate4min
Monitoring1min
Lab Intro: Distributed Training and Monitoring2min
Lab Review: Distributed Training and Monitoring7min
Quiz1 exercício prático
Module 2 Quiz8min
Clock
2 horas para concluir

Module 3: Scaling ML models with Cloud ML Engine

...
Reading
7 vídeos (Total de 28 min), 2 testes
Video7 videos
Why Cloud ML Engine?6min
Development Workflow1min
Packaging trainer3min
TensorFlow Serving3min
Lab: Scaling up MLmin
Lab Review: Scaling up ML10min
Quiz1 exercício prático
Module 3 Quiz4min
Clock
3 horas para concluir

Module 4: Feature Engineering

...
Reading
16 vídeos (Total de 92 min), 2 testes
Video16 videos
Good Features7min
Causality8min
Numeric5min
Enough Examples7min
Raw Data to Features1min
Categorical Features8min
Feature Crosses3min
Bucketizing3min
Wide and Deep5min
Where to do Feature Engineering3min
Feature Engineering Lab Overview3min
Feature Engineering Lab Review10min
Hyperparameter Tuning + Demo15min
ML Abstraction Levels4min
Summary1min
Quiz1 exercício prático
Module 4 Quiz6min
4.4
Direction Signs

67%

comecei uma nova carreira após concluir estes cursos
Briefcase

83%

consegui um benefício significativo de carreira com este curso
Money

33%

recebi um aumento ou promoção

Melhores avaliações

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!

Sobre 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....

Sobre o Programa de cursos integrados 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

Perguntas Frequentes – FAQ

  • Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

  • If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

  • Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

  • If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

  • This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid. If you’d like to take this course, but can’t afford the course fee, we encourage you to submit a financial aid application.

  • 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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