Gollapudi Sunila. - Practical Machine Learning
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About This Book
Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
Comprehensive practical solutions taking you into the future of machine learning
Go a step further and integrate your machine learning projects with Hadoop
What You Will Learn
Implement a wide range of algorithms and techniques for tackling complex data
Get to grips with some of the most powerful languages in data science, including R, Python, and Julia
Harness the capabilities of Spark and Mahout used in conjunction with Hadoop to manage and process data successfully
Apply the appropriate Machine learning technique to address a real-world problem
Get acquainted with deep learning and find out how neural networks are being used at the cutting edge of Machine learning
Explore the future of Machine learning and dive deeper into polyglot persistence, semantic data, and more
In Detail
This book explores an extensive range of Machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While Machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.
We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for modern data scientists who want to get to grips with Machine learnings real-world application.
The book also explores cutting-edge advances in Machine learning, with worked examples and guidance on Deep learning and Reinforcement learning, providing you with practical demonstrations and samples that help take the theory-and mystery-out of even the most advanced Machine learning methodologies.
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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 author 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: January 2016
Production reference: 2270116
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78439-968-9
www.packtpub.com
Author
Sunila Gollapudi
Reviewers
Rahul Agrawal
Rahul Jain
Ryota Kamoshida
Ravi Teja Kankanala
Dr. Jinfeng Yi
Commissioning Editor
Akram Hussain
Acquisition Editor
Sonali Vernekar
Content Development Editor
Sumeet Sawant
Technical Editor
Murtaza Tinwala
Copy Editor
Yesha Gangani
Project Coordinator
Shweta H Birwatkar
Proofreader
Safis Editing
Indexer
Tejal Daruwale Soni
Graphics
Jason Monteiro
Production Coordinator
Manu Joseph
Cover Work
Manu Joseph
Can machines think? This question has fascinated scientists and researchers around the world. In the 1950s, Alan Turing shifted the paradigm from "Can machines think?" to "Can machines do what humans (as thinking entities) can do?". Since then, the field of Machine learning/Artificial Intelligence continues to be an exciting topic and considerable progress has been made.
The advances in various computing technologies, the pervasive use of computing devices, and resultant Information/Data glut has shifted the focus of Machine learning from an exciting esoteric field to prime time. Today, organizations around the world have understood the value of Machine learning in the crucial role of knowledge discovery from data, and have started to invest in these capabilities.
Most developers around the world have heard of Machine learning; the "learning" seems daunting since this field needs a multidisciplinary thinkingBig Data, Statistics, Mathematics, and Computer Science. Sunila has stepped in to fill this void. She takes a fresh approach to mastering Machine learning, addressing the computing side of the equation-handling scale, complexity of data sets, and rapid response times.
Practical Machine Learning is aimed at being a guidebook for both established and aspiring data scientists/analysts. She presents, herewith, an enriching journey for the readers to understand the fundamentals of Machine learning, and manages to handhold them at every step leading to practical implementation path.
She progressively uncovers three key learning blocks. The foundation block focuses on conceptual clarity with a detailed review of the theoretical nuances of the disciple. This is followed by the next stage of connecting these concepts to the real-world problems and establishing an ability to rationalize an optimal application. Finally, exploring the implementation aspects of latest and best tools in the market to demonstrate the value to the business users.
V. Laxmikanth
Managing Director, Broadridge Financial Solutions (India) Pvt Ltd
Sunila Gollapudi works as Vice President Technology with Broadridge Financial Solutions (India) Pvt. Ltd., a wholly owned subsidiary of the US-based Broadridge Financial Solutions Inc. (BR). She has close to 14 years of rich hands-on experience in the IT services space. She currently runs the Architecture Center of Excellence from India and plays a key role in the big data and data science initiatives. Prior to joining Broadridge she held key positions at leading global organizations and specializes in Java, distributed architecture, big data technologies, advanced analytics, Machine learning, semantic technologies, and data integration tools. Sunila represents Broadridge in global technology leadership and innovation forums, the most recent being at IEEE for her work on semantic technologies and its role in business data lakes. Sunila's signature strength is her ability to stay connected with ever changing global technology landscape where new technologies mushroom rapidly , connect the dots and architect practical solutions for business delivery . A post graduate in computer science, her first publication was on Big Data Datawarehouse solution, Greenplum titled Getting Started with Greenplum for Big Data Analytics , Packt Publishing . She's a noted Indian classical dancer at both national and international levels, a painting artist, in addition to being a mother, and a wife.
At the outset, I would like to express my sincere gratitude to Broadridge Financial Solutions (India) Pvt Ltd., for providing the platform to pursue my passion in the field of technology.
My heartfelt thanks to Laxmikanth V, my mentor and Managing Director of the firm, for his continued support and the foreword for this book, Dr. Dakshinamurthy Kolluru, President, International School of Engineering (INSOFE), for helping me discover my love for Machine learning and Mr. Nagaraju Pappu, Founder & Chief Architect Canopus Consulting, for being my mentor in Enterprise Architecture.
This acknowledgement is incomplete without a special mention of Packt Publications for giving this opportunity to outline, conceptualize and provide complete support in releasing this book. This is my second publication with them, and again it is a pleasure to work with a highly professional crew and the expert reviewers.
To my husband, family and friends for their continued support as always. One person whom I owe the most is my lovely and understanding daughter Sai Nikita who was as excited as me throughout this journey of writing this book. I only wish there were more than 24 hours in a day and would have spent all that time with you Niki!
Lastly, this book is a humble submission to all the restless minds in the technology world for their relentless pursuit to build something new every single day that makes the lives of people better and more exciting.
Rahul Agrawal is a Principal Research Manager at Bing Sponsored Search in Microsoft India, where he heads a team of applied scientists solving problems in the domain of query understanding, ad matching, and large-scale data mining in real time. His research interests include large-scale text mining, recommender systems, deep neural networks, and social network analysis. Prior to Microsoft, he worked with Yahoo! Research, where he worked in building click prediction models for display advertising. He is a post graduate from Indian Institute of Science and has 13 years of experience in Machine learning and massive scale data mining.
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