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Machine Learning: Predict Poisonous Mushrooms using a Random Forest Model and the FFTrees Package in R
Coursera Project Network

Machine Learning: Predict Poisonous Mushrooms using a Random Forest Model and the FFTrees Package in R

Taught in English

Chris Shockley

Instructor: Chris Shockley

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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

(98 reviews)

What you'll learn

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

  • Practice data exploration using R and ggplot2.

  • Apply a Random Forest model using the FFTrees package in R.

Skills you'll practice

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

(98 reviews)

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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 Random Forest Model using R and the FFTrees package developed by Nathaniel Phillips. There will be a short discussion about the Interface and an Instructor Bio.

  2. Task 2: The Learners will get practice doing Exploratory Analysis 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 in R. The Instructor will show the Learner how to do it using the Base R way and also using a function from the caret package.

  4. Task 4: The Learner will get experience with the syntax of FFTrees package and then will execute the Random Forest Model.

  5. Task 5: The Learner will get practice with building a Confusion Matrix to evaluate model performance.

Recommended experience

Basic knowledge of Random Forest Models and Machine Learning

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Instructor

Instructor ratings
4.0 (5 ratings)
Chris Shockley
Coursera Project Network
10 Courses24,735 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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4.6

98 reviews

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