Machine Learning / 08
Distance-Based Learning
Explore distance measures, nearest neighbors, scaling, and high-dimensional data.
This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.
Subtopics
- 08.01
Distance-Based Learning
Planned guide: Distance-Based Learning. Focus on intuition, assumptions, and a worked example.
- 08.02
Euclidean Distance
Planned guide: Euclidean Distance. Focus on intuition, assumptions, and a worked example.
- 08.03
Manhattan Distance
Planned guide: Manhattan Distance. Focus on intuition, assumptions, and a worked example.
- 08.04
Minkowski Distance
Planned guide: Minkowski Distance. Focus on intuition, assumptions, and a worked example.
- 08.05
Cosine Similarity
Planned guide: Cosine Similarity. Focus on intuition, assumptions, and a worked example.
- 08.06
K-Nearest Neighbors
Planned guide: K-Nearest Neighbors. Focus on intuition, assumptions, and a worked example.
- 08.07
KNN Classification
Planned guide: KNN Classification. Focus on intuition, assumptions, and a worked example.
- 08.08
KNN Regression
Planned guide: KNN Regression. Focus on intuition, assumptions, and a worked example.
- 08.09
Choosing K
Planned guide: Choosing K. Focus on intuition, assumptions, and a worked example.
- 08.10
Scaling for KNN
Planned guide: Scaling for KNN. Focus on intuition, assumptions, and a worked example.
- 08.11
Curse of Dimensionality
Planned guide: Curse of Dimensionality. Focus on intuition, assumptions, and a worked example.