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Practical Machine Learning

 

Practical Machine Learning

Autor: Sunila Gollapudi
Broj strana: 468
ISBN broj: 9781784399689
Izdavač: PACKT PUBLISHING
Godina izdanja: 2016.

Pregleda (30 dana / ukupno): 45 / 1417

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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

Who This Book Is For

This book has been created for data scientists who want to see machine learning in action and explore its real-world 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 real-world problems
  • 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

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 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. Inside, a full exploration of the various algorithms gives you high-quality 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 real-world 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 cutting-edge 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.

Authors

Sunila Gollapudi

Sunila Gollapudi works as the vice president of technology at Broadridge Financial Solutions (India) Pvt. Ltd., a subsidiary of the US-based Broadridge Financial Solutions Inc (BR). She has over a decade of experience in developing, designing, and architecting data-driven solutions with her longest and strongest stint being in the BFSI domain. In her current role at Broadridge India, she’s responsible for driving architecture COE and plays a key role in big data and data science initiatives.

Sunila has held key positions at leading global organizations in the past. She’s considered to be a specialist in Java, distributed architecture, big data technologies, advanced analytics, machine learning, semantic technologies, and data integration tools. Her recent research activities involve working with semantic technologies to handle data management requirements using Business Data Lakes. Owing to her experience and strong knowledge in these areas, she’s been invited to many global conferences and meet-ups to talk about these widespread topics. Sunila has a master’s degree in computer application and has previously authored a book on Greenplum titled Getting Started with Greenplum for Big Data Analytics, Packt Publishing. She’s a trained Indian classical dancer who’s performed at both national and international levels, an avid traveler, a painting artist, a mother, and a wife.

Table of Contents

1: INTRODUCTION TO MACHINE LEARNING
2: MACHINE LEARNING AND LARGE-SCALE DATASETS
3: AN INTRODUCTION TO HADOOP'S ARCHITECTURE AND ECOSYSTEM
4: MACHINE LEARNING TOOLS, LIBRARIES, AND FRAMEWORKS
5: DECISION TREE BASED LEARNING
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
13: ENSEMBLE LEARNING
14: NEW GENERATION DATA ARCHITECTURES FOR MACHINE LEARNING

 

 

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