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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.
Prerequisites
Curriculum
Course outline
1
k-Nearest Neighbors (kNN)
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.
- Classification with Nearest Neighbors50 XP
- Recognizing a road sign with kNN100 XP
- Thinking like kNN50 XP
- Exploring the traffic sign dataset100 XP
- Classifying a collection of road signs100 XP
- What about the 'k' in kNN?50 XP
- Understanding the impact of 'k'50 XP
- Testing other 'k' values100 XP
- Seeing how the neighbors voted100 XP
- Data preparation for kNN50 XP
- Why normalize data?50 XP
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.
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.
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.
R
Supervised Learning in R: Classification
Course
Complete
