Mastering R

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Mastering Machine Learning With R

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Author : Cory Lesmeister
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-04-24

Mastering Machine Learning With R written by Cory Lesmeister 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 2017-04-24 with Computers categories.

Master machine learning techniques with R to deliver insights in complex projects About This Book Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning Implement advanced concepts in machine learning with this example-rich guide Who This Book Is For This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field. What You Will Learn Gain deep insights into the application of machine learning tools in the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Master fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Learn how to use R in a cloud service such as Amazon In Detail This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. Style and approach The book delivers practical and real-world solutions to problems and a variety of tasks such as complex recommendation systems. By the end of this book, you will have gained expertise in performing R machine learning and will be able to build complex machine learning projects using R and its packages.

Beginner S Guide To R Programming

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Author : Agasti Khatri
language : en
Publisher: Educohack Press
Release Date : 2025-02-20

Beginner S Guide To R Programming written by Agasti Khatri and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.

Discover the world of data analysis with "Beginner's Guide to R Programming." This comprehensive resource is crafted to help individuals learn the R programming language and explore its diverse applications. Whether you're a complete beginner or an experienced analyst, our book offers a structured learning path that starts with the basics and progresses to advanced topics like statistical analysis, data visualization, and machine learning. Each chapter includes practical examples, exercises, and real-world case studies, encouraging hands-on experimentation with R code. You'll delve into data types, functions, data manipulation, statistical analysis, data visualization, and more, building a solid foundation in R programming and data analysis. Complex concepts are explained in clear, easy-to-understand language, with visual aids, code snippets, and step-by-step tutorials to help you grasp key ideas effectively. The book emphasizes practical applications of R in real-world scenarios, showcasing how you can use R to solve problems, analyze data, make informed decisions, and communicate insights. With access to supplementary resources, including downloadable datasets, code samples, and additional exercises, you'll further enhance your learning experience and practice your skills.

Mastering R For Quantitative Finance

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Author : Edina Berlinger
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-03-10

Mastering R For Quantitative Finance written by Edina Berlinger 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 2015-03-10 with Computers categories.

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.

R For You

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Author : Leena Rokde
language : en
Publisher: Astrotech Lab
Release Date : 2025-01-01

R For You written by Leena Rokde and has been published by Astrotech Lab this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-01 with Computers categories.

Welcome to ‘R’ FOR YOU, a comprehensive guide to mastering R programming, designed to make your learning journey easy and enjoyable. This book serves as a one-stop resource for both beginners and seasoned programmers looking to harness the power of R for data analysis, statistical computing, and visualization. Whether you are a student, researcher, or industry professional, ‘R’ FOR YOU is structured to guide you step-by-step through R programming's versatile features. From the basics of syntax and data structures to advanced techniques for statistical modeling and machine learning, this book ensures that readers of all levels can gain a solid foundation and practical skills. Thank you for choosing ‘R’ FOR YOU. Let us embark on this exciting journey together!

Building A Recommendation System With R

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Author : Suresh K. Gorakala
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-09-29

Building A Recommendation System With R written by Suresh K. Gorakala 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 2015-09-29 with Computers categories.

Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn Get to grips with the most important branches of recommendation Understand various data processing and data mining techniques Evaluate and optimize the recommendation algorithms Prepare and structure the data before building models Discover different recommender systems along with their implementation in R Explore various evaluation techniques used in recommender systems Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

R For Programmers

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Author : Dan Zhang
language : en
Publisher: CRC Press
Release Date : 2017-03-31

R For Programmers written by Dan Zhang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-31 with Mathematics categories.

This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads readers to merge mature and effective methdologies in traditional programing to R programing. It shows how to interface R with C, Java, and other popular programing laguages and platforms.

Mastering Machine Learning With R

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Author : Cory Lesmeister
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-31

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications Key FeaturesBuild independent machine learning (ML) systems leveraging the best features of R 3.5Understand and apply different machine learning techniques using real-world examplesUse methods such as multi-class classification, regression, and clusteringBook Description Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work. What you will learnPrepare data for machine learning methods with easeUnderstand how to write production-ready code and package it for useProduce simple and effective data visualizations for improved insightsMaster advanced methods, such as Boosted Trees and deep neural networksUse natural language processing to extract insights in relation to textImplement tree-based classifiers, including Random Forest and Boosted TreeWho this book is for This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

Netsavvy

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Author : Ian Jukes
language : en
Publisher: Corwin Press
Release Date : 2000-05-19

Netsavvy written by Ian Jukes and has been published by Corwin Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-05-19 with Business & Economics categories.

This easy-to-follow guide can help students and teachers -- even the most technology-resistant -- learn to solve problems from sources like Internet sites, news groups, chat rooms, e-mail, and other Internet resources. Topics include: Creating your own lesson plans using sample lesson planners Applying frameworks for grade-level objectives and skills Dealing with information-technology overload Solving any information challenge with six critical steps Helping students harness the web with simple tips An important resource for today′s classroom, Net Savvy can help educators become leaders rather than followers in the new high-tech, high-speed, digital era.

Advanced R Second Edition

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Author : Hadley Wickham
language : en
Publisher: CRC Press
Release Date : 2019-05-24

Advanced R Second Edition written by Hadley Wickham and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-24 with Mathematics categories.

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising your code. By reading this book, you will learn: The difference between an object and its name, and why the distinction is important The important vector data structures, how they fit together, and how you can pull them apart using subsetting The fine details of functions and environments The condition system, which powers messages, warnings, and errors The powerful functional programming paradigm, which can replace many for loops The three most important OO systems: S3, S4, and R6 The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy, regardless of how your code is run How to find and remove performance bottlenecks The second edition is a comprehensive update: New foundational chapters: "Names and values," "Control flow," and "Conditions" comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them Much deeper coverage of metaprogramming, including the new tidy evaluation framework use of new package like rlang (http://rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming Use of color in code chunks and figures Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.

Machine Learning With R Cookbook

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Author : AshishSingh Bhatia
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-10-23

Machine Learning With R Cookbook written by AshishSingh Bhatia 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 2017-10-23 with Computers categories.

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book Apply R to simplify predictive modeling with short and simple code Use machine learning to solve problems ranging from small to big data Build a training and testing dataset, applying different classification methods. Who This Book Is For This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful. What You Will Learn Create and inspect transaction datasets and perform association analysis with the Apriori algorithm Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm Compare differences between each regression method to discover how they solve problems Detect and impute missing values in air quality data Predict possible churn users with the classification approach Plot the autocorrelation function with time series analysis Use the Cox proportional hazards model for survival analysis Implement the clustering method to segment customer data Compress images with the dimension reduction method Incorporate R and Hadoop to solve machine learning problems on big data In Detail Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier. Style and approach This is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.

Mastering R

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