What you'll get
- 2+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Core concepts of clustering analysis, including data preparation and feature selection
- Multiple clustering algorithms and iterative methods for building reliable clusters
- Techniques to interpret, evaluate, and refine clustering results for deeper insights
- Visualization methods, such as scatter plots, are used to present clustered data clearly
- How to structure and preprocess data for effective clustering
- Advanced clustering approaches to uncover complex patterns
- Skills to communicate clustering findings to stakeholders
- The Singular Covariance problem and practical ways to fix it
- Soft/Fuzzy K-Means clustering and when to use it
- Differences between single, complete, and Ward linkage methods
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Cluster Analysis: Segmentation of Smartphone Users | 2h 06m | ✔ | View Curriculum |
Description
This course offers a complete, hands-on introduction to clustering analysis. You will begin with an overview of the project objectives and the dataset, providing a clear understanding of the analysis's context and goals.
You will then move into the core clustering phases, where you will learn how to format data correctly, select meaningful features, and prepare a dataset for effective clustering. Across eight structured stages, you will apply different clustering algorithms and methods, refining and optimizing your clusters step by step.
Throughout the course, you will gain practical experience interpreting clustering results and understanding how clusters evolve through iterative improvements. By the final phase, you will bring everything together, consolidating results into meaningful clusters and using scatter plots to visualize patterns, relationships, and insights within the data.
The course concludes with a summary of findings and a discussion of their implications, helping you understand how to apply clustering techniques to real-world analysis and decision-making. By the end, you will have a strong foundation in clustering analysis and the confidence to use these methods on your own datasets.
Requirements
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Basic understanding of matrix arithmetic and probability
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Working knowledge of MS Excel for simple data handling
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Familiarity with basic statistics
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Comfort with analyzing numerical data
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Basic computer skills to run scripts and use analytical tools
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Prior exposure to Python or any programming language (optional but helpful).
Target Audience
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Students and professionals who want to learn machine learning and data science
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Anyone seeking an introduction to unsupervised learning and cluster analysis
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Individuals who want to learn how to write clustering algorithms
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Professionals working with large datasets to discover patterns automatically.
