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Digital. Etat : New. Second. Create scalable machine learning applications to power a modern data-driven business using Spark 2.xAbout This Book. Get to the grips with the latest version of Apache Spark. Utilize Spark's machine learning library to implement predictive analytics. Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.What You Will Learn. Get hands-on with the latest version of Spark ML. Create your first Spark program with Scala and Python. Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2. Access public machine learning datasets and use Spark to load, process, clean, and transform data. Use Spark's machine learning library to implement programs by utilizing well-known machine learning models. Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models. Write Spark functions to evaluate the performance of your machine learning modelsIn DetailThis book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.Style and approachThis practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.

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Digital. Etat : New. Second. Create scalable machine learning applications to power a modern data-driven business using Spark 2.xAbout This Book. Get to the grips with the latest version of Apache Spark. Utilize Spark's machine learning library to implement predictive analytics. Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.What You Will Learn. Get hands-on with the latest version of Spark ML. Create your first Spark program with Scala and Python. Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2. Access public machine learning datasets and use Spark to load, process, clean, and transform data. Use Spark's machine learning library to implement programs by utilizing well-known machine learning models. Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models. Write Spark functions to evaluate the performance of your machine learning modelsIn DetailThis book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.Style and approachThis practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.

Etat : New. Idioma/Language: Inglés. Create scalable machine learning applications to power a modern data-driven business using Spark 2. xAbout This Book* Get to the grips with the latest version of Apache Spark* Utilize Spark's machine learning library to implement predictive analytics* Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages. What You Will Learn* Get hands-on with the latest version of Spark ML* Create your first Spark program with Scala and Python* Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2* Access public machine learning datasets and use Spark to load, process, clean, and transform data* Use Spark's machine learning library to implement programs by utilizing well-known machine learning models* Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models* Write Spark functions to evaluate the performance of your machine learning modelsIn DetailThis book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business. Style and approachThis practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system. *** Nota: Los envíos a España peninsular, Baleares y Canarias se realizan a través de mensajería urgente. No aceptamos pedidos con destino a Ceuta y Melilla.

Etat : New. &Uumlber den AutorrnrnMohankumar Saraswatipura is a database solutions architect focusing on IBM Db2, Linux, Unix, Windows, and SAP HANA solutions. He is an IBM Champion (2010-2018) and a DB2 s Got Talent 2013 winner. He is also a frequent spea.

Digital. Etat : New. Second. Create scalable machine learning applications to power a modern data-driven business using Spark 2.xAbout This Book. Get to the grips with the latest version of Apache Spark. Utilize Spark's machine learning library to implement predictive analytics. Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.What You Will Learn. Get hands-on with the latest version of Spark ML. Create your first Spark program with Scala and Python. Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2. Access public machine learning datasets and use Spark to load, process, clean, and transform data. Use Spark's machine learning library to implement programs by utilizing well-known machine learning models. Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models. Write Spark functions to evaluate the performance of your machine learning modelsIn DetailThis book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.Style and approachThis practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.

Digital. Etat : New. Second. Create scalable machine learning applications to power a modern data-driven business using Spark 2.xAbout This Book. Get to the grips with the latest version of Apache Spark. Utilize Spark's machine learning library to implement predictive analytics. Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.What You Will Learn. Get hands-on with the latest version of Spark ML. Create your first Spark program with Scala and Python. Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2. Access public machine learning datasets and use Spark to load, process, clean, and transform data. Use Spark's machine learning library to implement programs by utilizing well-known machine learning models. Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models. Write Spark functions to evaluate the performance of your machine learning modelsIn DetailThis book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.Style and approachThis practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.

Etat : New. Print on Demand pp. 532.

Etat : New. PRINT ON DEMAND pp. 532.