Informatyka

R: Predictive Analysis. Master the art of predictive modeling

Tony Fischetti, Eric Mayor

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: Recipes for Analysis, Visualization and Machine Learning. Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning

Yu-Wei, Chiu (David Chiu), Atmajitsinh Gohil, Shanthi...

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

David Dossot

This book is a quick and concise introduction to RabbitMQ. Follow the unique case study of Clever Coney Media as they progressively discover how to fully utilize RabbitMQ, containing clever examples and detailed explanations. Whether you are someone who develops enterprise messaging products professionally or a hobbyist who is already familiar with open source Message Queuing software and you are looking for a new challenge, then this is the book for you. Although you should be familiar with Java, Ruby, and Python to get the most out of the examples, RabbitMQ Essentials will give you the push you need to get started that no other RabbitMQ tutorial can provide you with.

Rachunek macierzowy. Podręcznik dla studentów studiów licencjackich i inżynierskich

Marta Jarocka, Justyna Kozłowska, Beata Madras-Kobus, Anna...

Niniejszy podręcznik powstał z myślą o studentach Wydziału Inżynierii Zarządzania Politechniki Białostockiej kształcących się na kierunkach: logistyka, zarządzanie, zarządzanie i inżynieria produkcji, zarządzanie i inżynieria usług oraz inżynieria meblarstwa. Może on również służyć innym młodym adeptom matematyki – studentom studiów licencjackich i inżynierskich, którzy poznają tajniki rachunku macierzowego. Książka zawiera bowiem podstawowe treści, które są zgodne z obowiązującym programem przedmiotu matematyka na wielu kierunkach studiów.

Rachunek różniczkowy funkcji jednej zmiennej. Podręcznik dla studentów studiów licencjackich i inżynierskich

Beata Madras-Kobus, Marta Jarocka, Anna Małgorzata Olszewska

Pojęcie funkcji znane jest z nauki matematyki w szkole. Ale czy potrafimy obliczyć pochodne funkcji jednej zmiennej? Czy wiemy jak wykorzystać pochodne do badania przebiegu zmienności i narysowania wykresu funkcji? Jakie zastosowania mają pochodne w naukach ekonomicznych czy w fizyce? Odpowiedzi na te i wiele innych pytań dotyczących rachunku różniczkowego funkcji jednej zmiennej można znaleźć w podręczniku napisanym przez wykładowców z Międzynarodowej Katedry Logistyki i Inżynierii Usług Wydziału Inżynierii Zarządzania - dr Beatę Madras-Kobus, dr Martę Jarocką, prof. PB oraz dr inż. Annę M. Olszewską, prof. PB zatytułowanym Rachunek różniczkowy funkcji jednej zmiennej. Podręcznik dla studentów studiów licencjackich i inżynierskich. Podręcznik powstał z myślą o studentach Wydziału Inżynierii Zarządzania Politechniki Białostockiej kształcących się na kierunkach: logistyka, zarządzanie, zarządzanie i inżynieria produkcji, zarządzanie i inżynieria usług oraz zarządzanie finansami i rachunkowość.

RAG from First Principles. Engineering retrieval-augmented generation systems with Python, LangChain, and LlamaIndex

Jia Huang

Most developers can spin up a RAG pipeline in an afternoon using LangChain or LlamaIndex. Far fewer understand why retrieval fails or how to fix it. This book is for those who want to go deeper.'RAG From First Principles' dismantles the retrieval-augmented generation stack layer by layer, how documents are ingested and parsed, why chunking strategy directly impacts answer quality, how embedding models encode meaning, what happens inside a vector database, and how sparse and dense retrieval interact in a hybrid system. Written by Jia Huang, a research engineer and bestselling AI author, it brings research depth and production experience to one of AI's most critical engineering disciplines.Structured as a progressive dialogue between a seasoned engineer and two students, the book surfaces the questions practitioners actually ask. Each chapter builds on the last, from data import and chunking through embedding selection, index design, hybrid search, and post-retrieval processing, into response generation, evaluation, and advanced paradigms including GraphRAG, Agentic RAG, and Modular RAG.By the end, you'll have the architectural understanding to optimize, debug, and extend your RAG systems with confidence.