Supervised Learning in R: Classification

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

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4 Hours14 Videos55 Exercises64,758 Learners
3950 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.

  1. 1

    Chapter 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
  2. 2

    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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    Understanding Bayesian methods
    50 xp
    Computing probabilities
    100 xp
    Understanding dependent events
    50 xp
    A simple Naive Bayes location model
    100 xp
    Examining "raw" probabilities
    100 xp
    Understanding independence
    50 xp
    Understanding NB's "naivety"
    50 xp
    Who are you calling naive?
    50 xp
    A more sophisticated location model
    100 xp
    Preparing for unforeseen circumstances
    100 xp
    Understanding the Laplace correction
    50 xp
    Applying Naive Bayes to other problems
    50 xp
    Handling numeric predictors
    50 xp
  3. 3

    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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    Making binary predictions with regression
    50 xp
    Building simple logistic regression models
    100 xp
    Making a binary prediction
    100 xp
    The limitations of accuracy
    50 xp
    Model performance tradeoffs
    50 xp
    Calculating ROC Curves and AUC
    100 xp
    Comparing ROC curves
    50 xp
    Dummy variables, missing data, and interactions
    50 xp
    Coding categorical features
    100 xp
    Handling missing data
    100 xp
    Understanding missing value indicators
    50 xp
    Building a more sophisticated model
    100 xp
    Automatic feature selection
    50 xp
    The dangers of stepwise regression
    50 xp
    Building a stepwise regression model
    100 xp
  4. 4

    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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    Making decisions with trees
    50 xp
    Building a simple decision tree
    100 xp
    Visualizing classification trees
    100 xp
    Understanding the tree's decisions
    50 xp
    Growing larger classification trees
    50 xp
    Why do some branches split?
    50 xp
    Creating random test datasets
    100 xp
    Building and evaluating a larger tree
    100 xp
    Conducting a fair performance evaluation
    50 xp
    Tending to classification trees
    50 xp
    Preventing overgrown trees
    100 xp
    Creating a nicely pruned tree
    100 xp
    Why do trees benefit from pruning?
    50 xp
    Seeing the forest from the trees
    50 xp
    Understanding random forests
    50 xp
    Building a random forest model
    100 xp

In the following tracks

Data ScientistMachine Learning FundamentalsMachine Learning Scientist

Datasets

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

Collaborators

Nick CarchediNick Solomon

Prerequisites

Intermediate R
Brett Lantz Headshot

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.
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