4.4
stars
2,709 ratings

Google Cloud Training

43,538 already enrolled

Offered By

Google Cloud

About this Course

20,127 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.

Flexible deadlines

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.

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.

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 (169 Ratings)

Google Cloud Training

1,880,011 Learners

1,022 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%(3,504 ratings)

Week

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

Introduction22s

An ML recap2m

Training on large datasets with tf.data API3m

Working in-memory and with files3m

Getting the data ready for model training6m

Embeddings8m

Coursera: Getting Started with Google Cloud and Qwiklabs4m

Lab intro: TensorFlow Dataset API27s

Scaling data processing with tf.data and Keras preprocessing layers10m

Lab 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

Week

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

Introduction30s

Activation functions8m

Training neural networks with TensorFlow 2 and the Keras Sequential API7m

Serving models in the cloud3m

Lab intro: Introducing the Keras Sequential API on Vertex AI Platform33s

Training neural networks with TensorFlow 2 and the Keras Functional API9m

Lab intro: Build a DNN using the Keras Functional API on Vertex AI Platform29s

Model subclassing3m

(Optional) Lab intro: Making new layers and models via subclassing39s

Regularization basics4m

How 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

Week

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
1.51%

TOP REVIEWS FROM TENSORFLOW ON GOOGLE CLOUD

by AJNov 14, 2020

Excellent 'Introduction' to TensorFlow 2.0 (HINT: 'Introduction' does not mean 'Easy').

Evan Jones is at his best giving rapid intuitive explanations of advanced topics in deep neural networks.

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 VCMay 17, 2020

I feel this course very valuable because it taught how to create an automated service in cloud with very huge data and working with distributed systems in production environment with minimal time.

by SSJun 5, 2018

Nice introduce, might be more on introduce the model structure, because I still need to read additional notes to locate how to train my deep learning model online.

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