Book Image

Mastering Machine Learning with scikit-learn - Second Edition

By : Gavin Hackeling
Book Image

Mastering Machine Learning with scikit-learn - Second Edition

By: Gavin Hackeling

Overview of this book

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
9
From Decision Trees to Random Forests and Other Ensemble Methods
Index

Extracting features from images


Computer vision is the study and design of computational artifacts for processing and understanding images. These artifacts sometimes employ machine learning. An overview of computer vision is far beyond the scope of this book, but in this section, we will review some basic techniques used in computer vision for representing images in machine learning problems.

Extracting features from pixel intensities

A digital image is usually a raster, or pixmap, that maps colors to coordinates on a grid. That is, an image can be viewed as a matrix in which each element represents a color. A basic feature representation for an image can be constructed by reshaping the matrix into a vector by concatenating its rows together. Optical Character Recognition (OCR) is a canonical machine learning problem. Let's use this technique to create basic feature representations that can be used in an OCR application to recognize hand-written digits in character-delimited forms.

The digits...