Saturday, December 2, 2017

How to classify iris species using logistic regression

Despite its name, logistic regression can actually be used as a model for classification. In this post I will show you how to build a classification system in scikit-learn, and apply logistic regression to classify flower species from the famous Iris dataset.

Friday, November 24, 2017

Black Friday Sale

Some virtual turkey for all the nerds out there: Enjoy the eBook version of Machine Learning for OpenCV, OpenCV with Python Blueprints, and OpenCV: Computer Vision Projects with Python for only $10 each!

Monday, November 20, 2017

How to integrate essential scikit-learn functions with OpenCV

OpenCV's machine learning module provides a lot of important estimators such as support vector machines (SVMs) or random forest classifiers, but it lacks scikit-learn-style utility functions for interacting with data, scoring a classifier, or performing grid search with cross-validation. In this post I will show you how to wrap an OpenCV classifier as a scikit-learn estimator in five simple steps so that you can still make use of scikit-learn utility functions when working with OpenCV.

Thursday, October 19, 2017

OpenCV with Python Blueprints: 2nd Anniversary Giveaway

Two years ago today, Packt Publishing Ltd. released OpenCV with Python Blueprints, my first technical book on computer vision and machine learning using the OpenCV library. To celebrate this anniversary, I'm giving away a free copy of the book via Amazon Giveaways! Read on to find out how you can participate.


Michael Beyeler
OpenCV with Python Blueprints
Design and develop advanced computer vision projects using OpenCV with Python


Packt Publishing Ltd., London, England
Paperback: 230 pages
ISBN 978-178528269-0
[GitHub] [Discussion Group] [Free Sample]

Monday, September 11, 2017

Back to School Sale

Machine Learning for OpenCV is one of 5,000 titles you can currently get for only $10 at www.packtpub.com as part of their big Back to School sale. Grab a copy before it's too late!