Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques / Sunila Gollapudi ; foreword by V. Laxmikanth.

Description based on online resource; title from cover (Safari, viewed February 12, 2016).

Includes index.

Annotation Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout 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 HadoopWho This Book Is ForThis 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 moreIn DetailFinding 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 Naive 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 theoryand mysteryout of even the most advanced machine learning methodologies.Style and approachA practical data science tutorial designed to give yo.

Cover; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Preface; Chapter 1: Introduction to Machine learning; Machine learning; Definition; Core Concepts and Terminology; What is learning?; Data; Labeled and unlabeled data; Tasks; Algorithms; Models; Data and inconsistencies in Machine learning; Under-fitting; Over-fitting; Data instability; Unpredictable data formats; Practical Machine learning examples; Types of learning problems; Classification; Clustering; Forecasting, prediction or regression; Simulation; Optimization

Supervised learningUnsupervised learning; Semi-supervised learning; Reinforcement learning; Deep learning; Performance measures; Is the solution good?; Mean squared error (MSE); Mean absolute error (MAE); Normalized MSE and MAE (NMSE and NMAE); Solving the errors: bias and variance; Some complementing fields of Machine learning; Data mining; Artificial intelligence (AI); Statistical learning; Data science; Machine learning process lifecycle and solution architecture; Machine learning algorithms; Decision tree based algorithms; Bayesian method based algorithms; Kernel method based algorithms

Clustering methodsArtificial 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; Chapter 2: Machine learning and Large-scale datasets; Big data and the context of large-scale Machine learning; Functional versus Structural -- A methodological mismatch; Commoditizing information; Theoretical limitations of RDBMS; Scaling-up versus Scaling-out storage; Distributed and parallel computing strategies

Machine learning: Scalability and PerformanceToo many data points or instances; Too many attributes or features; Shrinking response time windows -- need for real-time responses; Highly complex algorithm; Feed forward, iterative prediction cycles; Model selection process; Potential issues in large-scale Machine learning; Algorithms and Concurrency; Developing concurrent algorithms; Technology and implementation options for scaling-up Machine learning; MapReduce programming paradigm; High Performance Computing (HPC) with Message Passing Interface (MPI)

Language Integrated Queries (LINQ) frameworkManipulating datasets with LINQ; Graphics Processing Unit (GPU); Field Programmable Gate Array (FPGA); Multicore or multiprocessor systems; Summary; Chapter 3: An Introduction to Hadoop's Architecture and Ecosystem; Introduction to Apache Hadoop; Evolution of Hadoop (the platform of choice); Hadoop and its core elements; 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

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