Java Data Science Made Easy

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Java Data Science Made Easy

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Author : Richard M. Reese
language : en
Publisher:
Release Date : 2017-07-07

Java Data Science Made Easy written by Richard M. Reese and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-07 with Computers categories.

Data collection, processing, analysis, and moreAbout This Book* Your entry ticket to the world of data science with the stability and power of Java* Explore, analyse, and visualize your data effectively using easy-to-follow examples* A highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks.Who This Book Is ForThis 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! What You Will Learn* Understand the key concepts of data science* Explore the data science ecosystem available in Java* Work with the Java APIs and techniques used to perform efficient data analysis* Find out how to approach different machine learning problems with Java* Process unstructured information such as natural language text or images, and create your own search* Learn how to build deep neural networks with DeepLearning4j* Build data science applications that scale and process large amounts of data* Deploy data science models to production and evaluate their performanceIn DetailData 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. You will cover a wide range of topics - from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings.By the end of this course, you will be up and running with various facets of data science using Java, in no time at all.This course contains premium content from two of our recently published popular titles:* Java for Data Science* Mastering Java for Data ScienceStyle and approachThis course follows a tutorial approach, providing examples of each of the concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

Java Data Science Made Easy

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Author : Richard M. Reese
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-07

Java Data Science Made Easy written by Richard M. Reese and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-07 with Computers categories.

Data collection, processing, analysis, and more About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples A highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks. Who This Book Is For This 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! What You Will Learn Understand the key concepts of data science Explore the data science ecosystem available in Java Work with the Java APIs and techniques used to perform efficient data analysis Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images, and create your own search Learn how to build deep neural networks with DeepLearning4j Build data science applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Data 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. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles: Java for Data Science Mastering Java for Data Science Style and approach This course follows a tutorial approach, providing examples of each of the concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

Integrating Technology Into Computer Science Education

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Author : Lillian N. Cassel
language : en
Publisher:
Release Date : 1997

Integrating Technology Into Computer Science Education written by Lillian N. Cassel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computer networks categories.

Java For Data Science

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Author : Richard M. Reese
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-10

Java For Data Science written by Richard M. Reese and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-10 with Computers categories.

Examine the techniques and Java tools supporting the growing field of data science About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples Make your Java applications more capable using machine learning Who This Book Is For This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful. What You Will Learn Understand the nature and key concepts used in the field of data science Grasp how data is collected, cleaned, and processed Become comfortable with key data analysis techniques See specialized analysis techniques centered on machine learning Master the effective visualization of your data Work with the Java APIs and techniques used to perform data analysis In Detail Data 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 book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book 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. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book. Style and approach This book follows a tutorial approach, providing examples of each of the major concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions

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Author : Matt Taddy
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-08-23

Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions written by Matt Taddy and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Business & Economics categories.

Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

The School Science Review

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Author :
language : en
Publisher:
Release Date : 2005

The School Science Review written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Electronic journals categories.

C C Users Journal

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Author :
language : en
Publisher:
Release Date : 2005

C C Users Journal written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with C (Computer program language) categories.

Forthcoming Books

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Author : Rose Arny
language : en
Publisher:
Release Date : 2003-04

Forthcoming Books written by Rose Arny and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-04 with American literature categories.

Machine Learning And Deep Learning Using Python And Tensorflow

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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29

Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.

Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory

Library Information Science Abstracts

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Author :
language : en
Publisher:
Release Date : 2002

Library Information Science Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Information science categories.