From the course: Machine Learning with Scikit-Learn
Join today to access over 22,500 courses taught by industry experts or purchase this course individually.
K-means clustering - scikit-learn Tutorial
From the course: Machine Learning with Scikit-Learn
K-means clustering
- [Instructor] Clustering algorithms have identified this thing groups of data. One example is who's clustering the group customers based on their behavior. There are so many clustering algorithms. But the most commonly used algorithm is K-Means. In this video, I'll show you how to use K-Means Clustering to find some underlying structure in your data. The first step is to import libraries. The next step is to load a dataset. This notebook uses the Iris data set. From there, you can arrange your data into a features matrix. It's important to note that K-Means is considered unsupervised learning algorithm. This means that you only need a features matrix. In the Iris data set, there are four features. In this notebook, the features matrix will only be two features, as it's easier to visualize clusters in two dimensions. It's important to mention that you do not eat a target factor, as this is an unsupervised learning…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.