About this Course
4.4
1,192 ratings
170 reviews
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 10 hours to complete

Suggested: 1 week of study, 8-12 hours/week...
Available languages

English

Subtitles: English

Skills you will gain

Machine LearningGoogle Cloud PlatformFeature EngineeringTensorflowCloud Computing
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 10 hours to complete

Suggested: 1 week of study, 8-12 hours/week...
Available languages

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
Hours to complete
11 minutes to complete

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 practice exercise
Machine Learning Course Pretest6m
Hours to complete
3 hours to complete

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 practice exercise
Module 1 Quiz8m
Hours to complete
5 hours to complete

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 Overview7s
TensorFlow Lab Review10m
Estimator API8m
Machine Learning with tf.estimator15s
Estimator Lab Review7m
Building Effective ML6m
Lab Intro: Refactoring to add batching and feature creation38s
Refactoring Lab Review4m
Train and Evaluate4m
Monitoring1m
Lab Intro: Distributed Training and Monitoring2m
Lab Review: Distributed Training and Monitoring7m
Quiz1 practice exercise
Module 2 Quiz8m
Hours to complete
2 hours to complete

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 ML39s
Lab Review: Scaling up ML10m
Quiz1 practice exercise
Module 3 Quiz4m
Hours to complete
3 hours to complete

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 practice exercise
Module 4 Quiz6m
4.4
170 ReviewsChevron Right
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Top Reviews

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 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!

About 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....

About the Data Engineering on Google Cloud Platform Specialization

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 on Google Cloud Platform

Frequently Asked Questions

  • 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, when available.

  • 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/

More questions? Visit the Learner Help Center.