| CRC Press

0원  | 20250523  | 9781498726436

This book provides a practical, hands-on introduction to the use of R in machine learning. Both supervised and unsupervised learning are comprehensively covered in the book, and the book explores key topics in machine learning, including support vector machines and neural networks. The book also describes various machine learning R packages and includes example code and use cases throughout.

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0원  | 20250523  | 9780367418298

This book is designed to introduce the concept of advanced business analytic approaches and would the first to cover the gamut of how to use the R programming language to apply descriptive, predictive, and prescriptive analytic methodologies for problem solving.

 | Editorial Academica Espanola

82,120원  | 20200527  | 9786200363602

El presente libro analiza mediante modelos matematicos de inteligencia artificial supervisada o Machine Learning distintos factores que afectan el desempeno escolar en un liceo secundario.

Lantz, Brett  | Packt Publishing

44,000원  | 20190415  | 9781788295864

A hands-on, readable guide to machine learning with R. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights and make new predictions. The 3rd edition is fully updated to R 3.6 and features newer and better libraries, advice on ethical and bias issues, and an ...

 | Springer-Nature New York Inc

139,590원  | 20181209  | 9789811349508

This book helps readers understand the mathematics of machine learning, and apply them in different situations. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis.

Springer  | Springer

164,230원  | 20171229  | 9789811068072

This book helps readers understand the mathematics of machine learning, and apply them in different situations. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis.

 | Springer Nature B.V.

72,980원  | 20190117  | 9781484242162

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.

Brett Lantz  | Packt

49,000원  | 20150731  | 9781784393908

Key Features *Harness the power of R for statistical computing and data science *Explore, forecast, and classify data with R *Use R to apply common machine learning algorithms to real-world scenarios Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R??”a cross-platform, zero-cost statistical programming environment??”there has never been a better time to start applying machine learning to your data. Whether you are new to data analytics or a veteran, machine learning with R offers a powerful set of methods to quickly and easily gain insights from your data. Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to the cutting-edge power you need to master exceptional machine learning techniques. Updated and upgraded to the latest libraries and most modern thinking, the second edition of Machine Learning with R provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book you??™ll discover all the analytical tools you need to gain insights from complex data and learn how to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you??™ll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R. What you will learn -Harness the power of R to build common machine learning algorithms with real-world data science applications -Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results -Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems -Classify your data with Bayesian and nearest neighbour methods -Predict values by using R to build decision trees, rules, and support vector machines -Forecast numeric values with linear regression, and model your data with neural networks -Evaluate and improve the performance of machine learning models -Learn specialized machine learning techniques for text mining, social network data, big data, and more

BERTRAMS PRINT ON DEMAND  | Packt Publishing

100,350원  | 20130713  | 9781782162148

Machine learning with R will transform the way you think about data. With the help of practical examples, you'll prepare data for analysis, choose a machine learning method, and measure the success of the process. The book will show you how to use the analytical tools you need to gain insights from complex data quickly.

Dr.Sunil Kumar Chinnamgari  | Packt Publishing

38,000원  | 20190111  | 9781789807943

The purpose of the book is to help a machine learning practitioner gets hands-on experience in working with real-world data and apply modern machine learning algorithms. You will learn to implement each algorithm to a specific industry problem. It covers projects involving both supervised as well as unsupervised learning approaches.

조민호  | 정보문화사

18,000원  | 20210220  | 9788956749037

입문자를 위한 R 데이터 분석 프로그래밍! 데이터 분석을 하기 위한 도구와 분석 요령을 배우기 위한 입문자들을 위한 도서로, 복잡한 수학이나 어려운 통계학적 이론은 빼고 꼭 필요한 정보만 이해할 수 있도록 예제와 함께 설명하고 있다. 특히 R을 활용하여 머신러닝의 지도학습과 비지도 학습을 구분하여 자세하게 설명하기 때문에, 연습 문제를 따라해 보면서 자신만의 결과값을 완성해 볼 수 있다. 그리고 R의 핵심 명령어 23개 및 효과적인 분석을 지원하는 데이터 조작 패키지를 알아보고, 통계 분석의 개념과 표본 생성 및 기초 통계량에 대해서도 자세하게 알아본다. 기본적인 설명 이후에는 통계학의 기본인 회귀 분석을 응용하여 신경망을 공부하며 딥러닝과 관련된 기술적 흐름에 대해서도 학습해 볼 수 있다. R을 시작하는 초심자가 활용하기 적합한 구성의 도서이다.

Fred Nwanganga, Mike Chapple  | Wiley

50,010원  | 20200429  | 9781119591511

Boehmke, Brad, Greenwell, Brandon M.  | CRC Press

192,780원  | 20191111  | 9781138495685

This book is designed to introduce the concept of advanced business analytic approaches and would the first to cover the gamut of how to use the R programming language to apply descriptive, predictive, and prescriptive analytic methodologies for problem solving.

Brindha Priyadarshini Jeyaraman  | Packt Publishing

45,000원  | 20200201  | 9781838550134

With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you'll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you'll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.

Reghav Bali, Dipanjan Sarkar  | Packt

32,000원  | 20160808  | 9781784390846

Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully About This Book * Get to grips with the concepts of machine learning through exciting real-world examples * Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning * Learn to build your own machine learning system with this example-based practical guide Who This Book Is For If you