Learning scikit-learn: Machine Learning in Python

✍ Scribed by Raúl Garreta, Guillermo Moncecchi

• Publisher: Packt
• Year: 2013
• Tongue: English
• Leaves: 118
• Category: Library

✦ Synopsis

Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of “big data”, huge amounts of information is being generated. This makes machine learning an unavoidable source of new data-based approximations for problem solving.With Learning scikit-learn: Machine Learning in Python, you will learn to incorporate machine learning in your applications. The book combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikit-learn and Python. The book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques.You will learn to evaluate your results and apply advanced techniques for preprocessing data. You will also be able to select the best set of features and the best methods for each problem. With Learning scikit-learn: Machine Learning in Python you will learn how to use the Python programming language and the scikit-learn library to build applications that learn from experience, applying the main concepts and techniques of machine learning.

✦ Table of Contents

Cover......Page 1
Copyright......Page 3
Credits......Page 4
About the Authors......Page 5
About the Reviewers......Page 7
www.PacktPub.com......Page 8
Table of Contents......Page 10
Preface......Page 12
Chapter 1: Machine Learning – A Gentle Introduction......Page 16
Installing scikit-learn......Page 17
Linux......Page 18
Checking your installation......Page 19
Our first machine learning method:
linear classification......Page 21
Evaluating our results......Page 27
Machine learning categories......Page 31
Important concepts related to
machine learning......Page 32
Summary......Page 34
Image recognition with Support
Vector Machines......Page 36
Training a Support Vector Machine......Page 39
Text classification with Naïve Bayes......Page 44
Preprocessing the data......Page 46
Training a Naïve Bayes classifier......Page 47
Evaluating the performance......Page 51
Explaining Titanic hypothesis with decision trees......Page 52
Preprocessing the data......Page 54
Training a decision tree classifier......Page 58
Interpreting the decision tree......Page 60
Random Forests – randomizing decisions......Page 62
Evaluating the performance......Page 63
Predicting house prices with regression......Page 64
First try – a linear model......Page 66
Second try – Support Vector Machines
for regression......Page 68
Third try – Random Forests revisited......Page 69
Evaluation......Page 70
Summary......Page 71
Chapter 3: Unsupervised Learning......Page 72
Principal Component Analysis......Page 73
Clustering handwritten digits
with k-means......Page 78
Alternative clustering methods......Page 85
Summary......Page 88
Chapter 4: Advanced Features......Page 90
Feature extraction......Page 91
Feature selection......Page 95
Model selection......Page 99
Grid search......Page 105
Parallel grid search......Page 106
Summary......Page 110
Index......Page 112