Java: Data Science Made Easy:
bData collection, processing, analysis, and more/bh2About This Book/h2ulliYour entry ticket to the world of data science with the stability and power of Java/liliExplore, analyse, and visualize your data effectively using easy-to-follow examples/liliA highly practical course covering a broad set of...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2017
|
| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bData collection, processing, analysis, and more/bh2About This Book/h2ulliYour entry ticket to the world of data science with the stability and power of Java/liliExplore, analyse, and visualize your data effectively using easy-to-follow examples/liliA highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks./li/ulh2Who This Book Is For/h2This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you! h2What You Will Learn/h2ulliUnderstand the key concepts of data science/liliExplore the data science ecosystem available in Java/liliWork with the Java APIs and techniques used to perform efficient data analysis/liliFind out how to approach different machine learning problems with Java/liliProcess unstructured information such as natural language text or images, and create your own search/liliLearn how to build deep neural networks with DeepLearning4j/liliBuild data science applications that scale and process large amounts of data/liliDeploy data science models to production and evaluate their performance/li/ulh2In Detail/h2Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. |
| Beschreibung: | 1 Online-Ressource (715 Seiten) |
| ISBN: | 9781788479189 |
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| 520 | |a bData collection, processing, analysis, and more/bh2About This Book/h2ulliYour entry ticket to the world of data science with the stability and power of Java/liliExplore, analyse, and visualize your data effectively using easy-to-follow examples/liliA highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks./li/ulh2Who This Book Is For/h2This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. | ||
| 520 | |a If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you! h2What You Will Learn/h2ulliUnderstand the key concepts of data science/liliExplore the data science ecosystem available in Java/liliWork with the Java APIs and techniques used to perform efficient data analysis/liliFind out how to approach different machine learning problems with Java/liliProcess unstructured information such as natural language text or images, and create your own search/liliLearn how to build deep neural networks with DeepLearning4j/liliBuild data science applications that scale and process large amounts of data/liliDeploy data science models to production and evaluate their performance/li/ulh2In Detail/h2Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. | ||
| 520 | |a It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. | ||
| 650 | 4 | |a COMPUTERS / Data Modeling & | |
| 650 | 4 | |a Design | |
| 650 | 4 | |a COMPUTERS / Databases / Data Mining | |
| 700 | 1 | |a Reese, Jennifer L. |e Sonstige |4 oth | |
| 700 | 1 | |a Grigorev, Alexey |e Sonstige |4 oth | |
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| isbn | 9781788479189 |
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| spelling | Reese, Richard M. Verfasser aut Java: Data Science Made Easy Reese, Richard M. 1 Birmingham Packt Publishing Limited 2017 1 Online-Ressource (715 Seiten) txt rdacontent c rdamedia cr rdacarrier bData collection, processing, analysis, and more/bh2About This Book/h2ulliYour entry ticket to the world of data science with the stability and power of Java/liliExplore, analyse, and visualize your data effectively using easy-to-follow examples/liliA highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks./li/ulh2Who This Book Is For/h2This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you! h2What You Will Learn/h2ulliUnderstand the key concepts of data science/liliExplore the data science ecosystem available in Java/liliWork with the Java APIs and techniques used to perform efficient data analysis/liliFind out how to approach different machine learning problems with Java/liliProcess unstructured information such as natural language text or images, and create your own search/liliLearn how to build deep neural networks with DeepLearning4j/liliBuild data science applications that scale and process large amounts of data/liliDeploy data science models to production and evaluate their performance/li/ulh2In Detail/h2Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. COMPUTERS / Data Modeling & Design COMPUTERS / Databases / Data Mining Reese, Jennifer L. Sonstige oth Grigorev, Alexey Sonstige oth |
| spellingShingle | Reese, Richard M. Java: Data Science Made Easy COMPUTERS / Data Modeling & Design COMPUTERS / Databases / Data Mining |
| title | Java: Data Science Made Easy |
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| title_short | Java: Data Science Made Easy |
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