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). For the planned 2nd edition, we added Diego Garat as a new author.

• Chapter 1 - A Gentle Introduction to Machine Learning

• Chapter 1 (2nd ed.) - A Gentle Introduction to Machine Learning with Python and Scikit-learn - Extended version, including classification, clustering and regression!. Warning:Python 3

• [Chapter 2 - Supervised Learning - Image Recognition with Support Vector Machines] (http://nbviewer.ipython.org/github/gmonce/scikit-learn-book/blob/master/Chapter%202%20-%20Supervised%20Learning%20-%20Image%20Recognition%20with%20Support%20Vector%20Machines.ipynb)

• Chapter 2 - Supervised Learning - Regression

• [Chapter 2 - Supervised Learning - Text Classification with Naive Bayes] (http://nbviewer.ipython.org/github/gmonce/scikit-learn-book/blob/master/Chapter%202%20-%20Supervised%20Learning%20-%20Text%20Classification%20with%20Naive%20Bayes.ipynb)

• [Chapter 2 - Supervised Learning - Explaining Titanic Hypothesis with Decision Trees] (http://nbviewer.ipython.org/github/gmonce/scikit-learn-book/blob/master/Chapter%202%20-%20Supervised%20learning%20-%20Explaining%20Titanic%20Hypothesis%20with%20Decision%20Trees.ipynb)

• Chapter 3 - Unsupervised Learning - Clustering Handwritten Digits

• Chapter 3 - Unsupervised Learning - Principal Component Analysis

• Chapter 4 - Advanced Features - Feature Engineering and Selection

• Chapter 4 - Advanced Features - Model Selection