K-means clustering is a method of vector quantization. In this video, learn how to create a K-Means model using the Python library scikit-learn to find some structure in your data.
- [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 algorithm. … Like a lot of different algorithms, …
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- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)