Instructor: Mohammad Torabi
Outline
In this module, you will:
- Understand the challenges of learning from high-dimensional data and learn about tools to mitigate the issue.
- Understand the goal of clustering and the concept of grouping similar data points.
Questions you will be able to answer after taking this module
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What is the “curse of dimensionality”, and how might it affect the performance of my model?
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How can I reduce the dimensionality of my data while preserving as much information as possible?
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How does K-means clustering work, step by step?
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How should I choose the right number of clusters in when applying clustering?
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How can I evaluate the quality of my clustering results?
Material
Link to Clustering exercises Link to Clustering slides