R Machine Learning By Example. Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

Dipanjan Sarkar, Raghav Bali

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.

R Machine Learning Essentials. Gain quick access to the machine learning concepts and practical applications using the R development environment

Michele Usuelli

R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

Dr. Sunil Kumar Chinnamgari

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.

R: Mining spatial, text, web, and social media data. Create and customize data mining algorithms

Nathan H. Danneman, Richard Heimann, Pradeepta Mishra, Bater Makhabel

Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects.After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Learning Data Mining with R by Bater Makhabel ? R Data Mining Blueprints by Pradeepta Mishra? Social Media Mining with R by Nathan Danneman and Richard Heimann

R Object-oriented Programming. A practical guide to help you learn and understand the programming techniques necessary to exploit the full power of R

Kelly Black

R: Predictive Analysis. Master the art of predictive modeling

Eric Mayor, Tony Fischetti, Rui Miguel Forte

Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines.The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you’ll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:• Data Analysis with R, Tony Fischetti• Learning Predictive Analytics with R, Eric Mayor• Mastering Predictive Analytics with R, Rui Miguel Forte

R Programming By Example. Practical, hands-on projects to help you get started with R

Omar Trejo Navarro

R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R.We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization.By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.

R Programming Fundamentals. Deal with data using various modeling techniques

Kaelen Medeiros

R Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. You’ll start by understanding how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops.Once you have grasped the basics, you’ll move on to studying data visualization and graphics. You’ll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you’ll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values.By the end of this book, you’ll have completed an entire data science project of your own for your portfolio or blog.

R: Recipes for Analysis, Visualization and Machine Learning. Click here to enter text

Shanthi Viswanathan, Atmajitsinh Gohil, Viswa Viswanathan, Yu-Wei, ...

The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We’ll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We’ll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we’ll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:• R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan• R Data Visualization Cookbook by Atmajitsinh Gohil• Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)

R Statistics Cookbook. Over 100 recipes for performing complex statistical operations with R 3.5

Francisco Juretig

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools.You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making.By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.

R: Unleash Machine Learning Techniques. Smarter data analytics

Brett Lantz, Cory Lesmeister, Dipanjan Sarkar, Raghav Bali

R is the established language of data analysts and statisticians around the world. And you shouldn’t be afraid to use it…This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you’ll get to grips with the fundamentals of R. This means you’ll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.For the following two modules we’ll begin to investigate machine learning algorithms in more detail. To build upon the basics, you’ll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they’re all focused on solving real problems in different areas, ranging from finance to social media.This Learning Path has been curated from three Packt products:• R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar• Machine Learning with R - Second Edition By Brett Lantz• Mastering Machine Learning with R By Cory Lesmeister

R Web Scraping Quick Start Guide. Techniques and tools to crawl and scrape data from websites

Olgun Aydin

Web scraping is a technique to extract data from websites. It simulates the behavior of a website user to turn the website itself into a web service to retrieve or introduce new data. This book gives you all you need to get started with scraping web pages using R programming.You will learn about the rules of RegEx and Xpath, key components for scraping website data. We will show you web scraping techniques, methodologies, and frameworks. With this book's guidance, you will become comfortable with the tools to write and test RegEx and XPath rules. We will focus on examples of dynamic websites for scraping data and how to implement the techniques learned. You will learn how to collect URLs and then create XPath rules for your first web scraping script using rvest library. From the data you collect, you will be able to calculate the statistics and create R plots to visualize them. Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. You will create AWS instances and use R to connect a PostgreSQL database hosted on AWS. By the end of the book, you will be sufficiently confident to create end-to-end web scraping systems using R.

RabbitMQ Cookbook. Knowing a reliable enterprise messaging system based on the AMQP standard can be an essential for today's software developers. This cookbook helps you learn all the basics of RabbitMQ through recipes, code, and real-life examples

Gabriele Santomaggio, Sigismondo Boschi

RabbitMQ is an open source message broker software (sometimes called message-oriented middleware) that implements the Advanced Message Queuing Protocol (AMQP). The RabbitMQ server is written in the Erlang programming language and is built on the Open Telecom Platform framework for clustering and failover. Messaging enables software applications to connect and scale. Applications can connect to each other as components of a larger application or to user devices and data.RabbitMQ Cookbook touches on all the aspects of RabbitMQ messaging. You will learn how to use this enabling technology for the solution of highly scalable problems dictated by the dynamic requirements of Web and mobile architectures, based for example on cloud computing platforms. This is a practical guide with several examples that will help you to understand the usefulness and the power of RabbitMQ.This book helps you learn the basic functionalities of RabbitMQ with simple examples which describe the use of RabbitMQ client APIs and how a RabbitMQ server works. You will find examples of RabbitMQ deployed in real-life use-cases, where its functionalities will be exploited combined with other technologies. This book helps you understand the advanced features of RabbitMQ that are useful for even the most demanding programmer. Over the course of the book, you will learn about the usage of basic AMQP functionalities and use RabbitMQ to let decoupled applications exchange messages as per enterprise integration applications. The same building blocks are used to implement the architecture of highly scalable applications like today's social networks, and they are presented in the book with some examples. You will also learn how to extend RabbitMQ functionalities by implementing Erlang plugins.This book combines information with detailed examples coupled with screenshots and diagrams to help you create a messaging application with ease.

RabbitMQ Essentials. Build distributed and scalable applications with message queuing using RabbitMQ - Second Edition

Lovisa Johansson, David Dossot

RabbitMQ is an open source message queuing software that acts as a message broker using the Advanced Message Queuing Protocol (AMQP). This book will help you to get to grips with RabbitMQ to build your own applications with a message queue architecture. You’ll learn from the experts from CloudAMQP as they share what they've learned while managing the largest fleet of RabbitMQ clusters in the world.Following the case study of Complete Car, you’ll discover how you can use RabbitMQ to provide exceptional customer service and user experience, and see how a message queue architecture makes it easy to upgrade the app and add features as the company grows. From implementing simple synchronous operations through to advanced message routing and tracking, you’ll explore how RabbitMQ streamlines scalable operations for fast distribution. This book will help you understand the advantages of message queue architecture, including application scalability, resource efficiency, and user reliability. Finally, you’ll learn best practices for working with RabbitMQ and be able to use this book as a reference guide for your future app development projects.By the end of this book, you’ll have learned how to use message queuing software to streamline the development of your distributed and scalable applications.

RabbitMQ Essentials. Hop straight into developing your own messaging applications by learning how to utilize RabbitMQ

David Dossot

Rabbits

Terry Miles

Kiedy śmiertelnie niebezpieczna sekretna gra zagraża rzeczywistości, świat może uratować tylko jeden człowiek Rabbits to gra tocząca się w alternatywnej rzeczywistości, tak rozległa, że korzysta z całego świata jako planszy. Od momentu jej rozpoczęcia odbyło się już dziesięć edycji, w których ogłoszono dziewięciu zwycięzców. Ich tożsamość jest jednak nieznana. Podobnie jak nagroda, którą mogły być rzekomo rekrutacja do NSA lub CIA, bogactwo, nieśmiertelność, a może nawet klucz do tajemnic wszechświata. Jednak im głębiej wnika się w grę, tym niebezpieczniejsza się staje. Gracze ginęli już w przeszłości, a liczba ofiar rośnie. Właśnie zbliża się rozpoczęcie jedenastej edycji. K, pasjonat Rabbits, od lat próbuje dostać się do gry. Miliarder Alan Scarpio, podający się za zwycięzcę szóstej edycji, twierdzi, że z grą dzieje się coś niedobrego i że K musi ją naprawić, zanim zacznie się rozgrywka w przeciwnym razie świat się rozpadnie. Wkrótce po zaginięciu Scarpia rozpoczyna się jedenasta edycja. A losy całego wszechświata spoczywają w rękach jednego bohatera Spora doza tajemniczości w czasach konspiracyjnego szaleństwa, przerażająca historia, która przekonująco przypomina nam o niebezpiecznej wiarygodności teorii spiskowych. Cory Doctorow, autor Małego brata Rabbits to powieść zręcznie napisana i pełna intryg. Łączy w sobie surrealizm Haruki Murakamiego z porywającym tempem i popkulturowymi wrzutkami z filmu Player One. Zapiera dech! Nicholas Eames, autor Królów Wyldu