Copyright (c) 2015-2017 Sebastian Raschka

https://github.com/rasbt/python-machine-learning-book

MIT License

Python Machine Learning - Code Examples

Chapter 2 - Training Machine Learning Algorithms for Classification

Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).

%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -p numpy,pandas,matplotlib

Sebastian Raschka 
last updated: 2017-03-10 

numpy 1.12.0
pandas 0.19.2
matplotlib 2.0.0

The use of watermark is optional. You can install this IPython extension via "pip install watermark". For more information, please see: https://github.com/rasbt/watermark.

Overview

• Artificial neurons - a brief glimpse into the early history of machine learning
• Implementing a perceptron learning algorithm in Python
  • Training a perceptron model on the Iris dataset
• Adaptive linear neurons and the convergence of learning
  • Minimizing cost functions with gradient descent
  • Implementing an Adaptive Linear Neuron in Python
  • Large scale machine learning and stochastic gradient descent
• Summary

from IPython.display import Image

Artificial neurons - a brief glimpse into the early history of machine learning

Image(filename='./images/02_01.png', width=500) 

Image(filename='./images/02_02.png', width=500)