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Supervised Learning in R: Classification

4.5+
24 reviews
Intermediate

In this course you will learn the basics of machine learning for classification.

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4 Hours14 Videos55 Exercises
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Course Description

This beginner-level introduction to machine learning covers four of the most common classification algorithms. You will come away with a basic understanding of how each algorithm approaches a learning task, as well as learn the R functions needed to apply these tools to your own work.
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In the following Tracks

Certification Available

Associate Data Scientist in R

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Machine Learning Fundamentals in R

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Machine Learning Scientist with R

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  1. 1

    k-Nearest Neighbors (kNN)

    Free

    As the kNN algorithm literally "learns by example" it is a case in point for starting to understand supervised machine learning. This chapter will introduce classification while working through the application of kNN to self-driving vehicle road sign recognition.

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    Classification with Nearest Neighbors
    50 xp
    Recognizing a road sign with kNN
    100 xp
    Thinking like kNN
    50 xp
    Exploring the traffic sign dataset
    100 xp
    Classifying a collection of road signs
    100 xp
    What about the 'k' in kNN?
    50 xp
    Understanding the impact of 'k'
    50 xp
    Testing other 'k' values
    100 xp
    Seeing how the neighbors voted
    100 xp
    Data preparation for kNN
    50 xp
    Why normalize data?
    50 xp
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Certification Available

Associate Data Scientist in R

Go To Track

Machine Learning Fundamentals in R

Go To Track

Machine Learning Scientist with R

Go To Track

Datasets

Lending Club loan dataTraffic sign image dataDonation dataBrett's location data

Collaborators

Collaborator's avatar
Nick Carchedi
Collaborator's avatar
Nick Solomon

Prerequisites

Intermediate R
Brett Lantz HeadshotBrett Lantz

Senior Data Scientist at Sony PlayStation

Brett Lantz currently works as a data scientist at Sony PlayStation, is the author of Machine Learning with R, and teaches machine learning at the Global School in Empirical Research Methods summer program. After training as a sociologist, Brett has applied his endless thirst for data to projects that involve understanding and predicting human behavior in fields including epidemiology, higher education fundraising, and most recently, the video gaming industry.
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*4.5
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  • Marialisa S.
    3 months

    It has been a well-organized course. It have balanced in a proper way the theory with exercises, with a good mixing of case studies and specific examples.

  • Thomas M.
    3 months

    highly informative, well arranged and perfect didactics

  • Jan R.
    4 months

    Covers four different supervised learning methods in R, with the purpose of classification, that is, assigning observations (data) to an outcome class. Examples of datasets covered in exercises are: road signs, credit applicants, potential charity donors, smartphone location data.

  • Jessa G.
    7 months

    DataCamp is an online data science and programming learning platform that offers a wide range of courses that cover a variety of technical subjects, such as Python, R, SQL, Data Science, Machine Learning, and AI. The platform has a user-friendly interface, and the courses are designed to be interactive, with hands-on projects and exercises that allow learners to apply what they have learned. The courses are taught by experienced professionals, and they provide a combination of video lectures, interactive exercises, and coding challenges. The platform has a flexible subscription model, which allows learners to choose the courses they want to take and also offers a 30-day free trial. Overall, reviews of DataCamp is an excellent resource for anyone interested in learning data science and programming, and it is highly recommended for both beginners and experienced professionals.

  • PAUL P.
    10 months

    Great course. I learned new modelling techniques with tidymodels. It's the first time I used this library for modelling.

"It has been a well-organized course. It have balanced in a proper way the theory with exercises, with a good mixing of case studies and specific examples."

Marialisa S.

"highly informative, well arranged and perfect didactics"

Thomas M.

"Covers four different supervised learning methods in R, with the purpose of classification, that is, assigning observations (data) to an outcome class. Examples of datasets covered in exercises are: road signs, credit applicants, potential charity donors, smartphone location data."

Jan R.

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