4.4
stars
2,714 ratings

Google Cloud Training

43,979 already enrolled

Offered By

Google Cloud

About this Course

13,611 recent views

This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

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Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Intermediate Level

Approx. 15 hours to complete

English

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What you will learn

Create TensorFlow and Keras machine learning models and understand their key components
Use the tf.data library to manipulate data and large datasets
Use the Keras Sequential and Functional APIs for simple and advanced model creation
Train, deploy, and productionalize ML models at scale with Vertex AI

Skills you will gain

Machine Learning
Python Programming
Build Input Data Pipeline
Tensorflow
keras

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

Approx. 15 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.5/5 (170 Ratings)

Google Cloud Training

1,924,077 Learners

1,038 Courses

Offered by

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.

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Syllabus - What you will learn from this course

Content Rating89%(3,516 ratings)

Week1

Week 1

1 minute to complete

Introduction to the Course

1 minute to complete

1 video (Total 1 min)

1 hour to complete

Introduction to the TensorFlow ecosystem

1 hour to complete

4 videos (Total 19 min), 1 reading, 1 quiz

5 hours to complete

Design and Build an Input Data Pipeline

Data is the a crucial component of a machine learning model. Collecting the right data is not enough. You also need to make sure you put the right processes in place to clean, analyze and transform the data, as needed, so that the model can take the most signal of it as possible. In this module we discuss training on large datasets with tf.data, working with in-memory files, and how to get the data ready for training. Then we discuss embeddings, and end with an overview of scaling data with tf.keras preprocessing layers.

5 hours to complete

10 videos (Total 41 min), 1 reading, 3 quizzes

10 videos

Introduction22sAn ML recap2mTraining on large datasets with tf.data API3mWorking in-memory and with files3mGetting the data ready for model training6mEmbeddings8mCoursera: Getting Started with Google Cloud and Qwiklabs4mLab intro: TensorFlow Dataset API27sScaling data processing with tf.data and Keras preprocessing layers10mLab intro: Classifying structured data using Keras preprocessing layers31s

1 reading

Resources: Design and Build a TensorFlow Input Data Pipeline10m

1 practice exercise

Design and Build Input Data Pipeline16m

Week2

Week 2

6 hours to complete

Building Neural Networks with the TensorFlow and Keras API

6 hours to complete

11 videos (Total 45 min), 1 reading, 4 quizzes

11 videos

Introduction30sActivation functions8mTraining neural networks with TensorFlow 2 and the Keras Sequential API7mServing models in the cloud3mLab intro: Introducing the Keras Sequential API on Vertex AI Platform33sTraining neural networks with TensorFlow 2 and the Keras Functional API9mLab intro: Build a DNN using the Keras Functional API on Vertex AI Platform29sModel subclassing3m(Optional) Lab intro: Making new layers and models via subclassing39sRegularization basics4mHow can we meaure model complexity: L1 vs. L2 Regularization 5m

1 reading

Resources: Building Neural Networks with the TensorFlow and Keras API10m

1 practice exercise

Building Neural Networks in TensorFlow with Keras API18m

Week3

Week 3

2 hours to complete

Training at Scale with Vertex AI

2 hours to complete

3 videos (Total 7 min), 1 reading, 2 quizzes

1 hour to complete

Summary

1 hour to complete

4 readings

Reviews

4.4

332 reviews

5 stars
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3 stars
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2 stars
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1 star
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TOP REVIEWS FROM TENSORFLOW ON GOOGLE CLOUD

by DWOct 16, 2018

pretty good. some of the code in the last lab could be better explained. also please debug the cloud shell, as it does not always show the "web preview" button ;) otherwise, good job!

by SRAug 2, 2018

Nice start. Have a look at Andrew NG's course, especially how they do the labs. Might give you some ideas for your next class versions.

by JZNov 3, 2022

Quite a technical course with sophisticated lab sessions, but I got good hands-on experience on building NN models using Keras and TF functional API as well as deploying the model in Vertex AI.

by PRDec 26, 2018

Amazing course! The short length of videos makes it lot easier for students to follow! Google is honestly the best at whatever it does! :)

View all reviews

Frequently Asked Questions

Can I preview a course before enrolling?
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.
What will I get when I enroll?
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.
When will I receive my 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.
Why can’t I audit this course?
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.

More questions? Visit the Learner Help Center.

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TensorFlow on Google Cloud

About this Course

What you will learn

Skills you will gain