hands-on lab
Performing K-Means Clustering With Python
Difficulty: Intermediate
Duration: Up to 1 hour
Students: 244
Rating: 4/5
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Description
K-Means learning is a machine learning technique used to divide a dataset into clusters to analyze its results. This classification algorithm divides a large group of data into smaller groups to maximize the similarity between data points. We will walk through applying and analyzing the K-Means clustering algorithm on a set of data using the Python libraries: pandas, scikit-learn, and matplotlib.
Learning Objectives
Upon completion of this lab you will be able to:
- Utilize Python to prepare data for Cluster Machine Learning
- Perform K-Means Clustering on a set of data
- Plotting the outcome of the K-Means clustering
Intended Audience
This lab is intended for:
- Data engineers
- Machine learning practitioners
- Anyone interested in using Python to perform clustering
Prerequisites
You should possess:
- A basic understanding of Python
- A basic understanding of K-Means Clustering
Updates
August 8th, 2024 - Added password protection to notebook
Covered topics
Lab steps
Signing In to the Google Cloud Console
Opening the Lab's Jupyter Notebook in Google Cloud