Learning scikit-learn: Machine Learning in Python.

Ipython sources for each chapter of the book

This repository holds all the ipython source and data for the "Learning scikit-learn: machine learning in Python" book, by Raúl Garreta and Guillermo Moncecchi (http://www.packtpub.com/learning-scikit-learn-machine-in-python/book)

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
data		data
Chapter 1 - A Gentle Introduction to Machine Learning.ipynb		Chapter 1 - A Gentle Introduction to Machine Learning.ipynb
Chapter 2 - Supervised Learning - Image Recognition with Support Vector Machines.ipynb		Chapter 2 - Supervised Learning - Image Recognition with Support Vector Machines.ipynb
Chapter 2 - Supervised Learning - Regression.ipynb		Chapter 2 - Supervised Learning - Regression.ipynb
Chapter 2 - Supervised Learning - Text Classification with Naive Bayes.ipynb		Chapter 2 - Supervised Learning - Text Classification with Naive Bayes.ipynb
Chapter 2 - Supervised learning - Explaining Titanic Hypothesis with Decision Trees.ipynb		Chapter 2 - Supervised learning - Explaining Titanic Hypothesis with Decision Trees.ipynb
Chapter 3 - Unsupervised Learning - Clustering Handwritten Digits.ipynb		Chapter 3 - Unsupervised Learning - Clustering Handwritten Digits.ipynb
Chapter 3 - Unsupervised Learning - Principal Component Analysis.ipynb		Chapter 3 - Unsupervised Learning - Principal Component Analysis.ipynb
Chapter 4 - Advanced Features - Feature Engineering and Selection.ipynb		Chapter 4 - Advanced Features - Feature Engineering and Selection.ipynb
Chapter 4 - Advanced Features - Model Selection.ipynb		Chapter 4 - Advanced Features - Model Selection.ipynb
Library versions, readme.ipynb		Library versions, readme.ipynb
README.md		README.md
titanic.png		titanic.png