Course
Cluster Analysis in Python
IntermediateSkill Level
Updated 04/2026PythonMachine Learning4 hr14 videos46 Exercises3,650 XP64,707Statement of Accomplishment
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Prerequisites
Intermediate Python1
Introduction to Clustering
Before you are ready to classify news articles, you need to be introduced to the basics of clustering. This chapter familiarizes you with a class of machine learning algorithms called unsupervised learning and then introduces you to clustering, one of the popular unsupervised learning algorithms. You will know about two popular clustering techniques - hierarchical clustering and k-means clustering. The chapter concludes with basic pre-processing steps before you start clustering data.
2
Hierarchical Clustering
This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The chapter concludes with a discussion on the limitations of hierarchical clustering and discusses considerations while using hierarchical clustering.
3
K-Means Clustering
This chapter introduces a different clustering algorithm - k-means clustering - and its implementation in SciPy. K-means clustering overcomes the biggest drawback of hierarchical clustering that was discussed in the last chapter. As dendrograms are specific to hierarchical clustering, this chapter discusses one method to find the number of clusters before running k-means clustering. The chapter concludes with a discussion on the limitations of k-means clustering and discusses considerations while using this algorithm.
4
Clustering in Real World
Now that you are familiar with two of the most popular clustering techniques, this chapter helps you apply this knowledge to real-world problems. The chapter first discusses the process of finding dominant colors in an image, before moving on to the problem discussed in the introduction - clustering of news articles. The chapter concludes with a discussion on clustering with multiple variables, which makes it difficult to visualize all the data.
Cluster Analysis in Python
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FAQs
Which Python library is used for clustering in this course?
The course primarily uses the SciPy library to implement both hierarchical and k-means clustering algorithms, along with standard tools for data visualization.
What dataset will I use to practice clustering?
You will explore player statistics from the FIFA 18 video game, applying clustering techniques to group players based on their performance attributes.
How will I determine the right number of clusters for my data?
For hierarchical clustering you will use dendrograms, and for k-means you will learn a separate method to evaluate the optimal number of clusters before running the algorithm.
Does the course cover real-world clustering applications beyond sports data?
Yes. The final chapter applies clustering to find dominant colors in images and to group news articles by topic, demonstrating practical uses in different domains.
What preprocessing steps are taught before clustering?
You will learn essential preprocessing steps like feature scaling and data normalization that are necessary before applying distance-based clustering algorithms effectively.
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