Java: Data Science Made Easy. Data collection, processing, analysis, and more

Richard M. Reese, Jennifer L. Reese, Alexey Grigorev

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

Java for Data Science. Examine the techniques and Java tools supporting the growing field of data science

Richard M. Reese, Jennifer L. Reese

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions

Learning Java Functional Programming. Create robust and maintainable Java applications using the functional style of programming

Richard M. Reese

Functional programming is an increasingly popular technology that allows you to simplify many tasks that are often cumbersome and awkward using an object-oriented approach. It is important to understand this approach and know how and when to apply it. Functional programming requires a different mindset, but once mastered it can be very rewarding.This book simplifies the learning process as a problem is described followed by its implementation using an object-oriented approach and then a solution is provided using appropriate functional programming techniques. Writing succinct and maintainable code is facilitated by many functional programming techniques including lambda expressions and streams. In this book, you will see numerous examples of how these techniques can be applied starting with an introduction to lambda expressions. Next, you will see how they can replace older approaches and be combined to achieve surprisingly elegant solutions to problems.This is followed by the investigation of related concepts such as the Optional class and monads, which offer an additional approach to handle problems. Design patterns have been instrumental in solving common problems. You will learn how these are enhanced with functional techniques.To transition from an object-oriented approach to a functional one, it is useful to have IDE support. IDE tools to refactor, debug, and test functional programs are demonstrated through the chapters. The end of the book brings together many of these functional programming techniques to create a more comprehensive application. You will find this book a very useful resource to learn and apply functional programming techniques in Java.

Learning Network Programming with Java. Harness the hidden power of Java to build network-enabled applications with lower network traffic and faster processes

Richard M. Reese

Network-aware applications are becoming more prevalent and play an ever-increasing role in the world today. Connecting and using an Internet-based service is a frequent requirement for many applications. Java provides numerous classes that have evolved over the years to meet evolving network needs. These range from low-level socket and IP-based approaches to those encapsulated in software services.This book explores how Java supports networks, starting with the basics and then advancing to more complex topics. An overview of each relevant network technology is presented followed by detailed examples of how to use Java to support these technologies. We start with the basics of networking and then explore how Java supports the development of client/server and peer-to-peer applications. The NIO packages are examined as well as multitasking and how network applications can address practical issues such as security.A discussion on networking concepts will put many network issues into perspective and let you focus on the appropriate technology for the problem at hand. The examples used will provide a good starting point to develop similar capabilities for many of your network needs

Natural Language Processing with Java Cookbook. Over 70 recipes to create linguistic and language translation applications using Java libraries

Richard M. Reese

Natural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon Web Services (AWS). You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentence, or semantic word.

Natural Language Processing with Java. Explore various approaches to organize and extract useful text from unstructured data using Java

Richard M. Reese

Natural Language Processing with Java. Techniques for building machine learning and neural network models for NLP - Second Edition

AshishSingh Bhatia, Richard M. Reese

Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes.You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more.By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications.