Supervised Learning in R: Classification
In this course you will learn the basics of machine learning for classification.
- 4 hours
- 14 Videos
- 55 Exercises
- 35,239 Participants
- 3,950 XP
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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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Chapter 1: k-Nearest Neighbors (kNN)
FreeAs 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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Chapter 3: Logistic Regression
Logistic regression involves fitting a curve to numeric data to make predictions about binary events. Arguably one of the most widely used machine learning methods, this chapter will provide an overview of the technique while illustrating how to apply it to fundraising data.
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Chapter 2: Naive Bayes
Naive Bayes uses principles from the field of statistics to make predictions. This chapter will introduce the basics of Bayesian methods while exploring how to apply these techniques to iPhone-like destination suggestions.
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Chapter 4: Classification Trees
Classification trees use flowchart-like structures to make decisions. Because humans can readily understand these tree structures, classification trees are useful when transparency is needed, such as in loan approval. We'll use the Lending Club dataset to simulate this scenario.
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1
Chapter 1: k-Nearest Neighbors (kNN)
FreeAs 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.
-
Chapter 2: Naive Bayes
Naive Bayes uses principles from the field of statistics to make predictions. This chapter will introduce the basics of Bayesian methods while exploring how to apply these techniques to iPhone-like destination suggestions.
-
Chapter 3: Logistic Regression
Logistic regression involves fitting a curve to numeric data to make predictions about binary events. Arguably one of the most widely used machine learning methods, this chapter will provide an overview of the technique while illustrating how to apply it to fundraising data.
-
Chapter 4: Classification Trees
Classification trees use flowchart-like structures to make decisions. Because humans can readily understand these tree structures, classification trees are useful when transparency is needed, such as in loan approval. We'll use the Lending Club dataset to simulate this scenario.
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Brett Lantz
Data Scientist at the University of Michigan
Brett Lantz is a data scientist at the University of Michigan and the author of Machine Learning with R. After training as a sociologist, Brett has applied his endless thirst for data to projects that involve understanding and predicting human behavior.
See MoreCollaborators
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Nick Carchedi
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Nick Solomon