Learning From Data Made Easy With R
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Learning From Data Made Easy With R
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Author : N. D. Lewis
language : en
Publisher:
Release Date : 2016-03-15
Learning From Data Made Easy With R written by N. D. Lewis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-15 with categories.
Who Else Wants to Learn from Data the Easy Way? Start building smarter models today using R ! Master Learning from Data with this fun, practical, hands on guide. This book provides an accessible, hands on easy to follow guide to building machine learning models. It explains what learning from data is all about, why it should be part of your data science toolkit, and how to exploit it in your own research. It is designed for anyone who wishes to gain a practical understanding of the important modeling and prediction techniques that make up the increasingly lucrative discipline of data science. NO EXPERIENCE REQUIRED: Bestselling data scientist Dr. N. D Lewis cuts a clear path through the jargon, opening the way for you to discover, understand, apply and exploit the potential of data science in your own research. Everything you need to get started is contained within this book. If you want to accelerate your progress, discover the best in data science and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Explore, evaluate and exploit the core types of learning. Unleash the power of supervised learning. Design successful solutions with semi-supervised learning. Ignite your use of unsupervised learning. Stimulate your own ideas and help you innovate new solutions This hands on text is for individuals who want to master the subject in the minimum amount of time. It leverages the power of the FREE predictive analytic package R to provide you with the necessary tools to maximize your understanding, deepen your knowledge and unleash ideas to enhance your data science projects. THIS BOOK IS FOR YOU IS FOR YOU IF YOU WANT:: Real world applications that make sense. Examples to stimulate your thinking. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use. Learning from Data Made Easy with R is your very own hands on practical, tactical, easy to follow guide to mastery. This is an exciting time to be involved in data science. Buy this book today and join the data science revolution!
Data Mining With R
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Author : Luis Torgo
language : en
Publisher: CRC Press
Release Date : 2016-11-30
Data Mining With R written by Luis Torgo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-30 with Business & Economics categories.
Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.
Deep Learning Made Easy With R
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Author : N. D. Lewis
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-01-10
Deep Learning Made Easy With R written by N. D. Lewis and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-10 with categories.
Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today's top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Understand Deep Neural Networks Use Autoencoders Unleash the power of Stacked Autoencoders Leverage the Restricted Boltzmann Machine Develop Recurrent Neural Networks Master Deep Belief Networks Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide - the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R! Buy the book today. Your next big breakthrough using deep learning is only a page away!
Insights From Data With R
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Author : Owen L. Petchey
language : en
Publisher: Oxford University Press
Release Date : 2021-02-25
Insights From Data With R written by Owen L. Petchey and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-25 with Science categories.
Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.
Learning From Data
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Author : Vladimir S. Cherkassky
language : en
Publisher: Wiley-Interscience
Release Date : 1998-03-25
Learning From Data written by Vladimir S. Cherkassky and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-03-25 with Computers categories.
Accommodating both beginning and advanced students, this book establishes a general conceptual framework, in which various learning methods, from statistics, neural networks, and fuzzy logic can be applied--showing that a few fundamental principles underlie most new methods being proposed today in the fields of statistics, engineering, and computer science.
Statistical Learning From A Regression Perspective
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Author : Richard A. Berk
language : en
Publisher: Springer
Release Date : 2016-10-26
Statistical Learning From A Regression Perspective written by Richard A. Berk and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-26 with Mathematics categories.
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. All of the analyses included are done in R with code routinely provided.
Lp Type Methods For Optimal Transductive Support Vector Machines
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Author : Gennaro Esposito,PhD
language : en
Publisher: Gennaro Esposito,PhD
Release Date : 2014-01-17
Lp Type Methods For Optimal Transductive Support Vector Machines written by Gennaro Esposito,PhD and has been published by Gennaro Esposito,PhD this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-17 with categories.
Using R For Data Analysis In Social Sciences
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Author : Quan Li
language : en
Publisher: Oxford University Press
Release Date : 2018-05-09
Using R For Data Analysis In Social Sciences written by Quan Li and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-09 with Political Science categories.
Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.
Statistical Problems In Particle Physics Astrophysics And Cosmology
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Author : Louis Lyons
language : en
Publisher: Imperial College Press
Release Date : 2006
Statistical Problems In Particle Physics Astrophysics And Cosmology written by Louis Lyons and has been published by Imperial College Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Science categories.
These proceedings comprise current statistical issues in analyzing data in particle physics, astrophysics and cosmology, as discussed at the PHYSTAT05 conference in Oxford. This is a continuation of the popular PHYSTAT series; previous meetings were held at CERN (2000), Fermilab (2000), Durham (2002) and Stanford (2003).In-depth discussions on topical issues are presented by leading statisticians and research workers in their relevant fields. Included are invited reviews and contributed research papers presenting the latest, state-of-the-art techniques.
R Machine Learning By Example
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Author : Raghav Bali
language : en
Publisher:
Release Date : 2016
R Machine Learning By Example written by Raghav Bali and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Computers categories.
Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfullyAbout 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 guideWho This Book Is ForIf you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary.What You Will Learn* Utilize the power of R to handle data extraction, manipulation, and exploration techniques* Use R to visualize data spread across multiple dimensions and extract useful features* Explore the underlying mathematical and logical concepts that drive machine learning algorithms* Dive deep into the world of analytics to predict situations correctly* Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action* Write reusable code and build complete machine learning systems from the ground up* Solve interesting real-world problems using machine learning and R as the journey unfolds* Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data scienceIn DetailData 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.Style and approachThe book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.