Coursera Project Network
Machine Learning: Predict Numbers from Handwritten Digits using a Neural Network, Keras, and R
Coursera Project Network

Machine Learning: Predict Numbers from Handwritten Digits using a Neural Network, Keras, and R

Taught in English

Chris Shockley

Instructor: Chris Shockley

4,951 already enrolled

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

2 Hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.4

(71 reviews)

What you'll learn

  • Train and Test a Neural Network Model to read hand written numbers and return the digit.

  • Practice using One Hot Encoding to build a classifier.

  • Practice evaluating model performance.

Details to know

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

2 Hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.4

(71 reviews)

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Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
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About this Guided Project

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Task 1: In this task the Learner will be introduced to the Course Objectives, which is to how to execute a Neural Network on the MNIST Data Set. There will also be a short discussion about the Interface, loading packages, and an Instructor Bio.

  2. Task 2: The Learners will see what a Tensor looks like and then apply that knowledge to 60,000 hand written digits using Keras array_reshape() function.

  3. Task 3: The Learner will then create a classifier using one hot encoding.

  4. Task 4: The Learner will then build out the architecture for the Neural Network. Rectified Linear Unit ("RELU") and SoftMax will be used.

  5. Task 5: The Learner will then build out a loss optimizer function using cross_entropy.

  6. Task 6: The Learner will test to see how the model performed using a Confusion Matrix.Task 3: The Learner will get experience creating Testing and Training Data Sets. There are multiple ways to do this and the Instructor will go over two of them in this Task.

Recommended experience

Basic knowledge of Machine Learning

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Instructor

Chris Shockley
Coursera Project Network
10 Courses24,565 learners

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How you'll learn

  • Skill-based, hands-on learning

    Practice new skills by completing job-related tasks.

  • Expert guidance

    Follow along with pre-recorded videos from experts using a unique side-by-side interface.

  • No downloads or installation required

    Access the tools and resources you need in a pre-configured cloud workspace.

  • Available only on desktop

    This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.

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4.4

71 reviews

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S
5

Reviewed on Mar 4, 2021

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