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Curs

Unsupervised Learning in R

IntermediarNivel de competențe

Actualizat 07.2024

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Începe cursul gratuit

RMachine Learning

4 h

16 videoclipuri

49 Exerciții

3,600 XP

54,928

Certificat de realizare

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Descrierea cursului

Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible.

Cerințe prealabile

Introduction to R

1

Unsupervised learning in R

The k-means algorithm is one common approach to clustering. Learn how the algorithm works under the hood, implement k-means clustering in R, visualize and interpret the results, and select the number of clusters when it's not known ahead of time. By the end of the chapter, you'll have applied k-means clustering to a fun "real-world" dataset!

Welcome to the course!

Identify clustering problems

Introduction to k-means clustering

k-means clustering

Results of kmeans()

Visualizing and interpreting results of kmeans()

How k-means works and practical matters

Handling random algorithms

Selecting number of clusters

Introduction to the Pokemon data

Practical matters: working with real data

Review of k-means clustering

Începe capitolul

2

Hierarchical clustering

Hierarchical clustering is another popular method for clustering. The goal of this chapter is to go over how it works, how to use it, and how it compares to k-means clustering.

Introduction to hierarchical clustering

Hierarchical clustering with results

Selecting number of clusters

Interpreting dendrogram

Cutting the tree

Clustering linkage and practical matters

Linkage methods

Comparing linkage methods

Practical matters: scaling

Comparing kmeans() and hclust()

Review of hierarchical clustering

Începe capitolul

3

Dimensionality reduction with PCA

Principal component analysis, or PCA, is a common approach to dimensionality reduction. Learn exactly what PCA does, visualize the results of PCA with biplots and scree plots, and deal with practical issues such as centering and scaling the data before performing PCA.

Introduction to PCA

PCA using prcomp()

Results of PCA

Additional results of PCA

Visualizing and interpreting PCA results

Interpreting biplots (1)

Interpreting biplots (2)

Variance explained

Visualize variance explained

Practical issues with PCA

Practical issues: scaling

Additional uses of PCA and wrap-up

Începe capitolul

4

Putting it all together with a case study

The goal of this chapter is to guide you through a complete analysis using the unsupervised learning techniques covered in the first three chapters. You'll extend what you've learned by combining PCA as a preprocessing step to clustering using data that consist of measurements of cell nuclei of human breast masses.

Introduction to the case study

Preparing the data

Exploratory data analysis

Performing PCA

Interpreting PCA results

Variance explained

PCA review and next steps

Communicating PCA results

Hierarchical clustering of case data

Results of hierarchical clustering

Selecting number of clusters

k-means clustering and comparing results

Clustering on PCA results

Wrap-up and review

Începe capitolul

Unsupervised Learning in R

Curs
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