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Bastiaan Sjardin - Large Scale Machine Learning with Python

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Bastiaan Sjardin Large Scale Machine Learning with Python

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Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

About This Book
  • Design, engineer and deploy scalable machine learning solutions with the power of Python
  • Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework
  • Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale
Who This Book Is For

This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.

What You Will Learn
  • Apply the most scalable machine learning algorithms
  • Work with modern state-of-the-art large-scale machine learning techniques
  • Increase predictive accuracy with deep learning and scalable data-handling techniques
  • Improve your work by combining the MapReduce framework with Spark
  • Build powerful ensembles at scale
  • Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine
In Detail

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.

Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

Style and Approach

This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.

Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production.

This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

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Large Scale Machine Learning with Python

Large Scale Machine Learning with Python

Copyright 2016 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: July 2016

Production reference: 1270716

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78588-721-5

www.packtpub.com

Credits

Authors

Bastiaan Sjardin

Luca Massaron

Alberto Boschetti

Reviewers

Oleg Okun

Kai Londenberg

Commissioning Editor

Akram Hussain

Acquisition Editor

Sonali Vernekar

Content Development Editor

Sumeet Sawant

Technical Editor

Manthan Raja

Copy Editor

Tasneem Fatehi

Project Coordinator

Shweta H Birwatkar

Proofreader

Safis Editing

Indexer

Mariammal Chettiyar

Graphics

Disha Haria

Kirk D'Penha

Production Coordinator

Arvindkumar Gupta

Cover Work

Arvindkumar Gupta

About the Authors

Bastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has a MSc degree in cognitive science obtained at the University of Leiden together with on campus courses at Massachusetts Institute of Technology (MIT). In the past 5 years, he has worked on a wide range of data science and artificial intelligence projects. He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming languages of choice are Python and R. Currently, he is the cofounder of Quandbee (http://www.quandbee.com/), a company providing machine learning and artificial intelligence applications at scale.

Luca Massaron is a data scientist and marketing research director who is specialized in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience in solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about everything regarding data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essentials.

I would like to thank Yukiko and Amelia for their continued support, help, and loving patience.

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges that span from natural language processing (NLP) and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

About the Reviewer

Oleg Okun is a machine learning expert and an author/editor of four books, numerous journal articles, and conference papers. He has been working for more than a quarter of a century. During this time, Oleg 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, and credit-scoring analytics. He is interested in all aspects of distributed machine learning and the Internet of Things. Oleg currently lives and works in Hamburg, Germany, and is about to start a new job as a chief architect of intelligent systems. His favorite programming languages are Python, R, and Scala.

I would like to express my deepest gratitude to my parents for everything that they have done for me.

Kai Londenberg is a data science and big data expert with many years of professional experience. Currently, he is working as a data scientist at the Volkswagen Data Lab. Before that, he had the pleasure of being the lead data scientist at Searchmetrics, where Luca Massaron was a member of his team. Kai enjoys working with cutting-edge technologies, and while he is a pragmatic machine learning practitioner and software developer at work, he always enjoys staying up-to-date with the latest technologies and research in machine learning, AI, and related fields. You can find him on LinkedIn at https://www.linkedin.com/in/kailondenberg.

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Preface

"The nice thing about having a brain is that one can learn, that ignorance can be supplanted by knowledge, and that small bits of knowledge can gradually pile up into substantial heaps."

-- Douglas Hofstadter

Machine learning is often referred to as the part of artificial intelligence that actually works . Its aim is to find a function based on an existing set of data (training set) in order to predict outcomes of a previously unseen dataset (test set) with the highest possible correctness. This occurs either in the form of labels and classes (classification problems) or in the form of a continuous value (regression problems). Tangible examples of machine learning in real-life applications range from predicting future stock prices to classifying the gender of an author from a set of documents. Throughout this book, the most important machine learning concepts, together with methods suitable for larger datasets, will be made clear to the reader, thanks to practical examples in Python. We will look at supervised learning (classification & regression), as well as unsupervised learning (such as Principal Component Analysis (PCA), clustering, and topic modeling) that have been found to be applicable to larger datasets.

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