Machine Learning Fundamentals K-Nearest Neighbors
About
Understand what classification is and learn how to implement k-Nearest Neighbors (KNN) classification in Python using the scikit-learn library.
Learning Objectives
You will be able to:
- Understand how classification works.
- Describe the k-NN algorithm and the intuition behind it.
- Prepare data for k-NN algorithm using standardization techniques.
- Fit, evaluate, and generate predictions from a k-NN model in
scikit-learn.
Content
| Topic | Skills |
|---|---|
| Slides | - Understand the intuition behind the k-NN algorithm |
| K-Nearest Neighbors | - Prepare data for k-NN algorithm using standardization techniques - Implement KNN with scikit-learn |
Prerequisites
- Write and run Python code in a Jupyter notebook.
- Create basic Python functions.
- Have familiarity with
pandas. - Have familiarity with
scikit-learn.
References
📖 Reference Materials
Important note
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