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Group your data: Clustering using Python and scikit-learn

BeginnerGuided Project

Explore machine learning clustering methods with Python and scikit-learn, including K-means, hierarchical, and DBSCAN, and learn cluster visualization techniques using the Plotly and Seaborn libraries. This course equips you with the skills to uncover patterns in data and make insightful discoveries, enabling you to build targeted recommenders, identify hidden features, and detect anomalies within your own data.

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 78

Skills You Will Learn

  • Machine Learning, Python, Clustering, Data Visualization, sklearn, Scikit-learn

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • October 29, 2024
About this Guided Project
In this guided project, you will explore clustering algorithms—a core component of machine learning and data science. This project enables you to uncover hidden patterns in data and discover correlations and groups that are not immediately apparent.

Using practical application and theoretical discussion, this project examines algorithms like K-means, hierarchical, and DBSCAN and shows you how to apply them effectively using Python and scikit-learn. You'll build your proficiency in data preprocessing, model evaluation by visualization, and the exploration of real-world use cases such as market segmentation and anomaly detection. When you complete this project, you'll have a solid foundation in clustering techniques and be ready to tackle complex data challenges across various domains.

This hands-on project is based on the Learn clustering algorithms using Python and scikit-learn tutorial.


What you'll learn

After you complete the project, you will:
  • Understand the fundamentals of clustering algorithms and their application in data science.
  • Learn how to implement various clustering algorithms using Python and the scikit-learn library.
  • Develop skills in preprocessing data to ensure it is suitable for clustering analysis.
  • Gain practical experience in evaluating the performance and effectiveness of different clustering models.
  • Explore use cases like market segmentation and anomaly detection to showcase clustering's real-world applications.

What You'll Need

Basic knowledge of Python is required. 

Instructors

Kang Wang

Data Scientist

I am a Data Scientist in the IBM. I am also a PhD Candidate in the University of Waterloo.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.

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Lucy Xu

Data Scientist

I am a Data Scientist Intern at IBM. I am also currently in my fourth year at the University of Waterloo studying Statistics with a minor in Computing.

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