Machine Learning in Python
K-Means Clustering in Python
Sifting through unlabeled datasets can be a frustrating and time-consuming process. In this course, you will deploy a k-means clustering algorithm to automatically segment customers into separate clusters, allowing for tailor made communications for specific groups.
Advanced    7 Lessons    90 Minutes    CPD Credits
About This Course
Python has automated processes for breaking down unstructured and unlabeled datasets. In this course, you will examine k-means clustering as a method for segmenting your data into clusters for more efficient analysis.
You will explore the practical business applications of k-means clustering. You will examine how k-means segments data. You will also run a k-means model before presenting your clusters visually.
By the end of this course, you will have a powerful tool for automating much of the manual heavy lifting involved when working with large, unlabeled datasets. You will have an appreciation of the huge productivity gains to be made from deploying k-means clustering into your own business.
Learning Outcomes
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Understand the applications of k-means clustering
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Understand how k-means segments data
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Running a k-means model
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Visualize the k-means clusters
Lessons
1. Course Introduction
2. What is Clustering
4. Preparing the Data
5. Visualizing the Data
6. Running the Model
Read More
7. Expanding the Model
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