About this Course

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Learner Career Outcomes

40%

started a new career after completing these courses

43%

got a tangible career benefit from this course

12%

got a pay increase or promotion
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

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

Approx. 30 hours to complete
English
Subtitles: English, Spanish, Russian, Japanese

What you will learn

  • Learn best practices for using TensorFlow, a popular open-source machine learning framework

  • Build a basic neural network in TensorFlow

  • Train a neural network for a computer vision application

  • Understand how to use convolutions to improve your neural network

Skills you will gain

Computer VisionTensorflowMachine Learning

Learner Career Outcomes

40%

started a new career after completing these courses

43%

got a tangible career benefit from this course

12%

got a pay increase or promotion
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

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

Approx. 30 hours to complete
English
Subtitles: English, Spanish, Russian, Japanese

Instructor

Offered by

deeplearning.ai logo

deeplearning.ai

Syllabus - What you will learn from this course

Week
1

Week 1

6 hours to complete

A New Programming Paradigm

6 hours to complete
4 videos (Total 16 min), 5 readings, 3 quizzes
4 videos
A primer in machine learning3m
The ‘Hello World’ of neural networks5m
Working through ‘Hello World’ in TensorFlow and Python3m
5 readings
Before you begin: TensorFlow 2.0 and this course10m
From rules to data10m
Try it for yourself10m
Introduction to Google Colaboratory10m
Week 1 Resources10m
1 practice exercise
Week 1 Quiz
Week
2

Week 2

7 hours to complete

Introduction to Computer Vision

7 hours to complete
7 videos (Total 15 min), 6 readings, 3 quizzes
7 videos
An Introduction to computer vision2m
Writing code to load training data2m
Coding a Computer Vision Neural Network2m
Walk through a Notebook for computer vision3m
Using Callbacks to control training1m
Walk through a notebook with Callbacks1m
6 readings
Exploring how to use data10m
The structure of Fashion MNIST data10m
See how it's done10m
Get hands-on with computer vision1h
See how to implement Callbacks10m
Week 2 Resources10m
1 practice exercise
Week 2 Quiz
Week
3

Week 3

8 hours to complete

Enhancing Vision with Convolutional Neural Networks

8 hours to complete
6 videos (Total 19 min), 6 readings, 3 quizzes
6 videos
What are convolutions and pooling?2m
Implementing convolutional layers1m
Implementing pooling layers4m
Improving the Fashion classifier with convolutions4m
Walking through convolutions3m
6 readings
Coding convolutions and pooling layers10m
Learn more about convolutions10m
Getting hands-on, your first ConvNet10m
Try it for yourself1h
Experiment with filters and pools1h
Week 3 Resources10m
1 practice exercise
Week 3 Quiz
Week
4

Week 4

9 hours to complete

Using Real-world Images

9 hours to complete
9 videos (Total 27 min), 10 readings, 3 quizzes
9 videos
Understanding ImageGenerator4m
Defining a ConvNet to use complex images2m
Training the ConvNet with fit_generator2m
Walking through developing a ConvNet2m
Walking through training the ConvNet with fit_generator3m
Adding automatic validation to test accuracy4m
Exploring the impact of compressing images3m
A conversation with Andrew1m
10 readings
Explore an impactful, real-world solution10m
Designing the neural network10m
Train the ConvNet with ImageGenerator10m
Exploring the solution10m
Training the neural network10m
Experiment with the horse or human classifier1h
Get hands-on and use validation30m
Get Hands-on with compacted images30m
Week 4 Resources10m
Wrap up10m
1 practice exercise
Week 4 Quiz

About the TensorFlow in Practice Specialization

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization....
TensorFlow in Practice
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  • 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. .

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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