Julian Avila - Scikit-Learn Cookbook: Over 80 Recipes for Machine Learning in Python With Scikit-Learn
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with scikit-learn
BIRMINGHAM - MUMBAI
Copyright 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: November 2014
Second edition: November 2017
Production reference: 1141117
ISBN 978-1-78728-638-2
www.packtpub.com
| Authors Julian Avila Trent Hauck | Copy Editors Vikrant Phadkay Safis Editing |
| Reviewer Oleg Okun | Project Coordinator Nidhi Joshi |
| Commissioning Editor Amey Varangaonkar | Proofreader Safis Editing |
| Acquisition Editor Vinay Argekar | Indexer Tejal Daruwale Soni |
| Content Development Editor Mayur Pawanikar | Graphics Tania Dutta |
| Technical Editor Dinesh Pawar | Production Coordinator Aparna Bhagat |
Julian Avila is a programmer and data scientist in the fields of finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitar, machine learning, and artificial intelligence through discussions with friends in the CSAIL lab.
He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock market trading programs.
Special thanks to MIT professor emeritus Robert Rose, who was very encouraging in regards to writing this particular machine learning book. I would also like to thank professors Seth Lloyd and Peter Shor for introducing me to computations of a probabilistic nature, the many-worlds that might be of quantum mechanics; Dr. Paul Bamberg for teaching statistics (although I took a geometry class from him) and Dr. Michael Artin for his humor and geometric algebra knowledge. Finally, I would like to thank Dr. Yuri Chernyak who taught me a lot about problem solving.
I would like to thank Packt for writing (and helping me write) very direct and practical books. I would also like to thank the Python community and their philosophies. Python is a very welcoming and elegant language, particularly effective for solving very tough problems and fine-tuning requirements very fast. I would like to thank you in advance for reading this book and pushing the data science frontier further with scikit-learn.
Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas.
He is the author of the book Instant Data Intensive Apps with pandas How-to, by Packt Publishinga book that can get you up to speed quickly with pandas and other associated technologies.
Oleg Okun is a machine learning expert and an author/editor of four books, numerous journal articles, and conference papers. His career spans more than a quarter of a century.
He was employed in both academia and industry in his mother country, Belarus, and abroad (Finland, Sweden, and Germany). His work experience includes document image analysis, fingerprint biometrics, bioinformatics, online/offline marketing analytics, credit scoring analytics, and text analytics. He is interested in all aspects of distributed machine learning and the Internet of Things.
Oleg currently lives and works in Hamburg, Germany.
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