Distance Metrics Explained and Visualized in Python

Measuring the distance between two point is something almost trivial. However, there are many ways of doing that with each having it's strength!

# Table of Content
- Introduction: 0:00
- Euclidean Distance Theory: 0:58
- Euclidean Distance Code: 2:14

- Euclidean Distance Visualization: 3:17
- Manhattan Distance Theory: 5:02
- Manhattan Distance Code: 7:08
- Manhattan Distance Visualization: 7:41

- Chebyshev Distance Theory: 8:52
- Chebyshev Distance Code: 10:37
- Chebyshev Distance Visualization: 11:03

- Minkowski Distance Theory: 12:09
- Minkowski Distance Code: 12:09
- Minkowski Distance Visualization: 14:03

- Mahalanobis Distance Theory: 14:39
- Mahalanobis Distance Code: 17:22
- Mahalanobis Distance Visualization: 18:02
- Conclusion: 20:45


Github Link: https://github.com/yacineMahdid/artificial-intelligence-and-machine-learning

There exists many kind of distance that can be used with continuous data. The most 5 most common are:
- Euclidean Distance
- Manhattan Distance
- Chebyshev Distance
- Minkowski Distance
- Mahalanobis Distance
Each of them are related in some ways or another, with some being generalization. Using the right metric to describe the right distance in a problem set allows for having better results and/or better interpretation.

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