About this Course

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Learner Career Outcomes

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started a new career after completing these courses

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got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
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Flexible deadlines
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Intermediate Level
Approx. 19 hours to complete
English
Subtitles: English

What you will learn

  • Use the Keras Sequential and Functional APIs for simple and advanced model creation

  • Design and build a TensorFlow 2.x input data pipeline

  • Use the tf.data library to manipulate data and large datasets

  • Train, deploy, and productionalize ML models at scale with Cloud AI Platform

Skills you will gain

Machine LearningPython ProgrammingBuild Input Data PipelineTensorflowkeras

Learner Career Outcomes

33%

started a new career after completing these courses

38%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 19 hours to complete
English
Subtitles: English

Instructor

Offered by

Google Cloud logo

Google Cloud

Syllabus - What you will learn from this course

Content RatingThumbs Up90%(2,701 ratings)Info
Week
1

Week 1

7 minutes to complete

Introduction to course

7 minutes to complete
2 videos (Total 7 min)
2 videos
Getting Started with Google Cloud and Qwiklabs3m
3 hours to complete

Introduction to TensorFlow

3 hours to complete
5 videos (Total 22 min), 1 reading, 5 quizzes
5 videos
TensorFlow API Hierarchy4m
Components of TensorFlow: Tensors and Variables8m
Lab Intro Introduction to Tensors and Variables1m
Lab Intro Writing low-level TensorFlow programs43s
1 reading
Readings10m
3 practice exercises
Introduction to TensorFlow15m
API Hierarchy15m
Tensors and Variables15m
Week
2

Week 2

7 hours to complete

Design and Build a TensorFlow Input Data Pipeline

7 hours to complete
10 videos (Total 25 min), 1 reading, 9 quizzes
10 videos
Working in-memory and with files3m
Getting the data ready for model training6m
Lab Intro Load CSV and Numpy Data 28s
Lab Intro Loading Image Data54s
Lab Intro Feature Columns37s
Optional Lab Intro TFRecord and tf.Example1m
Training on Large Datasets with tf.data API4m
Lab Intro Manipulating data with Tensorflow Dataset API34s
Optional Lab Intro Feature Analysis Using TensorFlow Data Validation and Facets1m
1 reading
Readings15m
3 practice exercises
PRACTICE QUIZ: Getting the data ready for model training15m
Training on Large Datasets with tf.data API15m
Design and Build Input Data Pipeline15m
Week
3

Week 3

4 hours to complete

Training neural networks with Tensorflow 2 and the Keras Sequential API

4 hours to complete
7 videos (Total 25 min), 1 reading, 5 quizzes
7 videos
Activation functions8m
Activation functions: Pitfalls to avoid in Backpropagation 5m
Neural Networks with Keras Sequential API7m
Lab intro Keras Sequential API21s
Lab Intro Logistic Regression43s
Lab Intro Optional Lab Advanced Logistic Regression in TensorFlow 2.01m
1 reading
Readings10m
2 practice exercises
Activation Functions15m
Neural Networks with TF2 and Keras15m
Week
4

Week 4

3 hours to complete

Training neural networks with Tensorflow 2 and the Keras Functional API

3 hours to complete
6 videos (Total 29 min), 1 reading, 4 quizzes
6 videos
Regularization: The Basics4m
Regularization: L1, L2, and Early Stopping5m
Regularization: Dropout5m
Serving models in the Cloud3m
Lab intro Keras Functional API38s
1 reading
Readings1h
3 practice exercises
The Keras Functional API15m
Regularization15m
Serving Models in the Cloud15m
1 hour to complete

Summary

1 hour to complete
1 video (Total 8 min), 1 reading, 1 quiz
1 video
1 reading
Quiz Questions to ALL Lessons 10m
1 practice exercise
Course Summary15m

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About the Machine Learning with TensorFlow on Google Cloud Platform Specialization

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using 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 <...
Machine Learning with TensorFlow on Google Cloud Platform

Frequently Asked Questions

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  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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