Explore the five most common distance metrics for continuous data in this 21-minute video tutorial. Learn the theory, implementation, and visualization of Euclidean, Manhattan, Chebyshev, Minkowski, and Mahalanobis distances using Python. Understand how each metric relates to others and when to apply them for better results and interpretations in various problem sets. Follow along with code examples and visual representations to gain a comprehensive understanding of these essential distance measurement techniques in data analysis and machine learning.
Overview
Syllabus
- Introduction:
- Euclidean Distance Theory:
- Euclidean Distance Code:
- Euclidean Distance Visualization:
- Manhattan Distance Theory:
- Manhattan Distance Code:
- Manhattan Distance Visualization:
- Chebyshev Distance Theory:
- Chebyshev Distance Code:
- Chebyshev Distance Visualization:
- Minkowski Distance Theory:
- Minkowski Distance Code:
- Minkowski Distance Visualization:
- Mahalanobis Distance Theory:
- Mahalanobis Distance Code:
- Mahalanobis Distance Visualization:
- Conclusion:
Taught by
Yacine Mahdid
