4. K-Nearest Neighbours and K-Means Clustering

Syllabus Points Covered

Software automation

Algorithms in machine learning

• Explore models of training ML

  • unsupervised learning

• Investigate common applications of key ML algorithms

  • image recognition

• Describe types of algorithms associated with ML

  • K-nearest neighbour

Chapter Contents

• 4.1. K-Nearest Neighbours and K-Means Clustering
  • 4.1.1. Glossary
• 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
  • 4.4.2. Glossary
• 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
  • 4.10.3. Glossary
• 4.11. Extension: Image Data
• 4.12. Extension: Building a KNN Classification Model
  • 4.12.1. KNN Classification 1D
  • 4.12.2. KNN 2D
• 4.13. Unsupervised Learning: Clustering
  • 4.13.1. Glossary
• 4.14. Extension: The K-means Clustering Algorithm
  • 4.14.1. Visual Walkthrough
  • 4.14.2. Glossary
• 4.15. Extension: Building a K-means Clustering Model
• 4.16. Extension: Text Data