Alex Tellez - Mastering Machine Learning with Spark 2.x
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- Book:Mastering Machine Learning with Spark 2.x
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Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial
About This Book- Process and analyze big data in a distributed and scalable way
- Write sophisticated Spark pipelines that incorporate elaborate extraction
- Build and use regression models to predict flight delays
Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and small data machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark.
What You Will Learn- Use Spark streams to cluster tweets online
- Run the PageRank algorithm to compute user influence
- Perform complex manipulation of DataFrames using Spark
- Define Spark pipelines to compose individual data transformations
- Utilize generated models for off-line/on-line prediction
- Transfer the learning from an ensemble to a simpler Neural Network
- Understand basic graph properties and important graph operations
- Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language
- Use K-means algorithm to cluster movie reviews dataset
The purpose of machine
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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: August 2017
Production reference: 1290817
ISBN 978-1-78528-345-1
www.packtpub.com
Author Alex Tellez Max Pumperla Michal Malohlava | Copy Editor Muktikant Garimella |
| ReviewerDipanjan Deb | Project CoordinatorUlhas Kambali |
| Commissioning EditorVeena Pagare | ProofreaderSafis Editing |
| Acquisition EditorLarissa Pinto | IndexerRekha Nair |
| Content Development EditorNikhil Borkar | GraphicsJason Monteiro |
| Technical EditorDiwakar Shukla | Production CoordinatorMelwyn Dsa |
Alex Tellez is a life-long data hacker/enthusiast with a passion for data science and its application to business problems. He has a wealth of experience working across multiple industries, including banking, health care, online dating, human resources, and online gaming. Alex has also given multiple talks at various AI/machine learning conferences, in addition to lectures at universities about neural networks. When hes not neck-deep in a textbook, Alex enjoys spending time with family, riding bikes, and utilizing machine learning to feed his French wine curiosity!
First and foremost, Id like to thank my co-author, Michal, for helping me write this book. As fellow ML enthusiasts, cyclists, runners, and fathers, we both developed a deeper understanding of each other through this endeavor, which has taken well over one year to create. Simply put, this book would not have been possible without Michals support and encouragement.
Next, Id like to thank my mom, dad, and elder brother, Andres, who have been there every step of the way from day 1 until now. Without question, my elder brother continues to be my hero and is someone that I will forever look up to as being a guiding light. Of course, no acknowledgements would be finished without giving thanks to my beautiful wife, Denise, and daughter, Miya, who have provided the love and support to continue the writing of this book during nights and weekends. I cannot emphasize enough how much you both mean to me and how you guys are the inspiration and motivation that keeps this engine running. To my daughter, Miya, my hope is that you can pick this book up and one day realize that your old man isnt quite as silly as I appear to let on.
Last but not least, Id also like to give thanks to you, the reader, for your interest in this exciting field using this incredible technology. Whether you are a seasoned ML expert, or a newcomer to the field looking to gain a foothold, you have come to the right book and my hope is that you get as much out of this as Michal and I did in writing this work.
Max Pumperla is a data scientist and engineer specializing in deep learning and its applications. He currently works as a deep learning engineer at Skymind and is a co-founder of aetros.com. Max is the author and maintainer of several Python packages, including elephas, a distributed deep learning library using Spark. His open source footprint includes contributions to many popular machine learning libraries, such as keras, deeplearning4j, and hyperopt. He holds a PhD in algebraic geometry from the University of Hamburg.
Michal Malohlava, creator of Sparkling Water, is a geek and the developer; Java, Linux, programming languages enthusiast who has been developing software for over 10 years. He obtained his PhD from Charles University in Prague in 2012, and post doctorate from Purdue University.
During his studies, he was interested in the construction of not only distributed but also embedded and real-time, component-based systems, using model-driven methods and domain-specific languages. He participated in the design and development of various systems, including SOFA and Fractal component systems and the jPapabench control system.
Now, his main interest is big data computation. He participates in the development of the H2O platform for advanced big data math and computation, and its embedding into Spark engine, published as a project called Sparkling Water.
Dipanjan Deb is an experienced analytic professional with over 17 years of cumulative experience in machine/statistical learning, data mining and predictive analytics across finance, healthcare, automotive, CPG, automotive, energy, and human resource domains. He is highly proficient in developing cutting-edge analytic solutions using open source and commercial software to integrate multiple systems in order to provide massively parallelized and large-scale optimization.
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