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

• Chapter 2 - Supervised Learning - Regression

• Chapter 2 - Supervised Learning - Text Classification with Naive Bayes

• Chapter 2 - Supervised Learning - Explaining Titanic Hypothesis with Decision Trees

• 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