4. K-Nearest Neighbours and K-Means Clustering

• 4.1. K-Nearest Neighbours and K-Means Clustering
• 4.2. Distance and Similarity
• 4.3. Extension: The Problem With Distance Similarity
  • 4.3.1. Standardisation
• 4.4. KNN Regression 1D
  • 4.4.1. The ‘k’ in K-Nearest Neighbours
• 4.5. Visualising KNN Regression 1D (k = 1)
• 4.6. Extension: Visualising KNN Regression 1D (k = 2)
• 4.7. KNN Regression 2D
• 4.8. Extension: Building a KNN Regression Model
• 4.9. Extension: Selecting The Value of k
  • 4.9.1. Understanding The Bias
  • 4.9.2. Selecting k
• 4.10. KNN Classification
  • 4.10.1. KNN Classification In 1D
  • 4.10.2. KNN Classification In 2D
• 4.11. Extension: Image Data
• 4.12. Extension: Building a KNN Classification Model
  • 4.12.1. KNN Classification 1D
• 4.13. KNN 2D
• 4.14. Unsupervised Learning: Clustering
• 4.15. Extension: The K-means Clustering Algorithm
  • 4.15.1. Visual Walkthrough
• 4.16. Extension: Building a K-means Clustering Model
• 4.17. Extension: Text Data