There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page.

Option 1: Eclipse zip file of all code

Option 2: Choose what you want from here:

- Chapter 2 (Linear Discriminants):
- The Perceptron
- The Linear Regressor
- Another Perceptron (for use with logic.py)
- Demonstration of Perceptron with logic functions
- Demonstration of Linear Regressor with logic functions
- Demonstration of Perceptron with Pima Indian dataset
- Demonstration of Linear Regressor with auto-mpg dataset
- The numbers dataset Noisy versions of the numbers More noisy numbers Visualisation of the numbers dataset
- Training data for the prostate dataset Test data for the prostate dataset (variables are log cancer volume, log prostate weight, age, lbph, svi, lcp, Gleason score, pgg45 and the last one is response lpsa)

- Chapter 3 (The Multi-Layer Perceptron):
- The Multi-Layer Perceptron
- Demonstration of the MLP on logic functions
- Demonstration of the MLP for classification on the Iris dataset
- Demonstration of the MLP for regression on data from a sine wave
- Demonstration of the MLP for time series on the Palmerston North Ozone dataset
- The Palmerston North ozone dataset

- Chapter 4 (Radial Basis Functions):
- Chapter 6 (Learning with Trees):
- Chapter 7 (Decision by Committee: Ensemble Learning):
- Chapter 8 (Probability and Learning):
- Chapter 9 (Unsupervised Learning):
- Chapter 10 (Dimensionality Reduction):
- Chapter 11 (Optimisation and Search):
- Chapter 12 (Evolutionary Learning):
- Chapter 13 (Reinforcement Learning):
- Chapter 14 (Markov Chain Monte Carlo Methods):
- Chapter 15 (Graphical Models):