Coursera Project Network
Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set
Coursera Project Network

Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set

Taught in English

Chris Shockley

Instructor: Chris Shockley

Included with Coursera Plus

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
4.6

(32 reviews)

What you'll learn

  • Complete a random Training and Test Set from one Data Source using an R function.

  • Practice data distribution using R and ggplot2.

  • Apply a Neural Net model to the Data and examine the results by building a Confusion Matrix.

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
4.6

(32 reviews)

See how employees at top companies are mastering in-demand skills

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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
  • Build confidence using the latest tools and technologies
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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 using the NeuralNet R package on the MPG data set. There will be a short discussion about the Interface and an Instructor Bio.

  2. Task 2: The Learners will get experience looking at the data using ggplot2. This is important in order for the practitioner to see the balance of the data, especially as it relates to the Response Variable.

  3. 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.

  4. Task 4: The Learner will get experience with the syntax of the Neuralnet package in R by building out a neural net model. There will be a short discussion on the differences between the predict function in R and compute with the Neuralnet package as well.

  5. Task 5: The Learner will get experience evaluation models in this Task. The Confusion Matrix will be discussed as the evaluation metric of choice for the specific problem. The conclusion of the course will use the two evaluation metrics see how well the model performed on the test data set.

Recommended experience

Basic knowledge of Random Forest Models and Machine Learning

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Instructor

Chris Shockley
Coursera Project Network
10 Courses24,514 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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