Introduction to Machine Learning, Part 2: Unsupervised Machine Learning

From the series: Introduction to Machine Learning

Seth DeLand, MathWorks

Get an overview of unsupervised machine learning, which looks for patterns in datasets that don’t have labeled responses. You’d use this technique when you want to explore your data but don’t yet have a specific goal, or you’re not sure what information the data contains. It’s also a good way to reduce the dimensionality of your data. 

Most unsupervised learning techniques are a form of cluster analysis. Clustering algorithms fall into two broad groups: 

  • Hard clustering, where each data point belongs to only one cluster 

  • Soft clustering, where each data point can belong to more than one cluster

This video uses examples to illustrate hard and soft clustering algorithms, and it shows why you’d want to use unsupervised machine learning to reduce the number of features in your dataset.


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