After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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.
Prerequisites
Curriculum
Course outline
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!50 XP
- Identify clustering problems50 XP
- Introduction to k-means clustering50 XP
- k-means clustering100 XP
- Results of kmeans()100 XP
- Visualizing and interpreting results of kmeans()100 XP
- How k-means works and practical matters50 XP
- Handling random algorithms100 XP
- Selecting number of clusters100 XP
- Introduction to the Pokemon data50 XP
- Practical matters: working with real data100 XP
- Review of k-means clustering50 XP
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.
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.
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.
R
Unsupervised Learning in R
Course
Complete
