Chapter 4 Data, Models, and Learning

The second part of the book introduces the four pillars of machine learning:

1. Regression (Chapter 9)
   
2. Dimensionality Reduction (Chapter 10)
   
3. Density Estimation (Chapter 11)
   
4. Classification (Chapter 12)

This part connects the mathematical foundations from earlier chapters to practical algorithms that solve real-world tasks. The goal is to show how concepts from linear algebra, probability, and optimization enable the design of learning systems — not to explore every advanced method, but to establish a practical and mathematical grounding.

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