Advanced Modeling Clustering with K-Means
About
Understand what unsupervised learning is, when to apply clustering, and perform a k-means clustering, evaluating cluster quality using Silhouette Coefficient and Inertia.
Learning Objectives
You will be able to:
- Know the difference between supervised and unsupervised learning.
- Understand when to use clustering.
- Perform a k-Means clustering analysis.
- Evaluate clusters for fit with Silhouette Coefficient and Inertia.
Content
| Topic | Skills |
|---|---|
| Slides | - Conceptual overview of boosting |
| Clustering with K-Means | - Walkthrough of K-Means in scikit-learn |
Prerequisites
- Define basic principles of supervised learning.
- Prepare features and create models using
scikit-learn. - Plot data.
References
📖 Reference Materials
Important note
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