Book description
Tackle the realworld complexities of modern machine learning with innovative, cuttingedge, techniques
About This Book
 Fullycoded 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
Who This Book Is For
This book has been created for data scientists who want to see machine learning in action and explore its realworld application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.
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 Hadoop to manage and process data successfully
 Apply the appropriate machine learning technique to address realworld problems
 Get acquainted with Deep learning and find out how neural networks are being used at the cuttingedge of machine learning
 Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more
In Detail
Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development.
This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and realworld examples. While machine learning can be highly theoretical, this book offers a refreshing handson approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you highquality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data.
This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its realworld application.
With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data.
You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cuttingedge advancements 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.
Style and approach
A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.
Table of contents

Practical Machine Learning
 Table of Contents
 Practical Machine Learning
 Credits
 Foreword
 About the Author
 Acknowledgments
 About the Reviewers
 www.PacktPub.com
 Preface

1. Introduction to Machine learning
 Machine learning
 Performance measures
 Some complementing fields of Machine learning
 Machine learning process lifecycle and solution architecture

Machine learning algorithms
 Decision tree based algorithms
 Bayesian method based algorithms
 Kernel method based algorithms
 Clustering methods
 Artificial neural networks (ANN)
 Dimensionality reduction
 Ensemble methods
 Instance based learning algorithms
 Regression analysis based algorithms
 Association rule based learning algorithms
 Machine learning tools and frameworks
 Summary

2. Machine learning and Largescale datasets
 Big data and the context of largescale Machine learning
 Algorithms and Concurrency
 Technology and implementation options for scalingup Machine learning
 Summary

3. An Introduction to Hadoop's Architecture and Ecosystem
 Introduction to Apache Hadoop

Machine learning solution architecture for big data (employing Hadoop)
 The Data Source layer
 The Ingestion layer
 The Hadoop Storage layer
 The Hadoop (Physical) Infrastructure layer – supporting appliance
 Hadoop platform / Processing layer
 The Analytics layer
 The Consumption layer
 MapReduce
 Hadoop 2.x
 Summary

4. Machine Learning Tools, Libraries, and Frameworks
 Machine learning tools – A landscape
 Apache Mahout
 R
 Julia
 Python
 Apache Spark
 Spring XD
 Summary

5. Decision Tree based learning
 Decision trees
 Implementing Decision trees
 Summary
 6. Instance and Kernel Methods Based Learning
 7. Association Rules based learning
 8. Clustering based learning
 9. Bayesian learning
 10. Regression based learning
 11. Deep learning

12. Reinforcement learning
 Reinforcement Learning (RL)
 Reinforcement learning solution methods
 Summary
 13. Ensemble learning
 14. New generation data architectures for Machine learning
 Index
Product information
 Title: Practical Machine Learning
 Author(s):
 Release date: January 2016
 Publisher(s): Packt Publishing
 ISBN: 9781784399689
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