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If you want to learn how to use R’s machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful.Master machine learning techniques with R to deliver insights for complex projectsAbout This BookGet to grips with the application of Machine Learning methods using an extensive set of R packagesUnderstand the benefits and potential pitfalls of using machine learning methodsImplement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML systemWhat You Will LearnGain deep insights to learn the applications of machine learning tools to the industryManipulate data in R efficiently to prepare it for analysisMaster the skill of recognizing techniques for effective visualization of dataUnderstand why and how to create test and training data sets for analysisFamiliarize yourself with fundamental learning methods such as linear and logistic regressionComprehend advanced learning methods such as support vector machinesRealize why and how to apply unsupervised learning methodsIn DetailMachine learning is a field of Artificial Intelligence to build systems that learn from data.
Learn all of Excel’s statistical toolsTest your hypotheses and draw conclusionsUse Excel to give meaning to your dataUse Excel to interpret statsStatistical analysis with Excel is incredibly useful—and this book shows you that it can be easy, too! You’ll discover how to use Excel’s perfectly designed tools to analyze and understand data, predict trends, make decisions, and more. Tackle the technical aspects of Excel and start using them to interpret your data!Inside…Covers Excel 2016 for Windows® & Mac® usersCheck out new Excel stuffMake sense of worksheetsCreate shortcutsTool around with analysisUse Quick StatisticsGraph your dataWork with probabilityHandle random variables
Authors: Davey, S., Gordon, N., Holland, I., Rutten, M., Williams, J.Presents a unique insight into the Bayesian calculation of the search zone for MH370Written by members of the MH370 search teamContains a tutorial description of available data allowing readers to work out their own solutionsThis book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated.
Statistical data is only as valuable as your ability to analyze, interpret, and present it in a meaningful way. Gnuplot is the most widely used program to plot and visualize data for Unix/Linux systems and it is also popular for Windows and the Mac. It’s open-source (as in free!), actively maintained, stable, and mature. It can deal with arbitrarily large data sets and is capable of producing high-quality, publication-ready graphics.So far, the only comprehensive documentation available about gnuplot is the online reference documentation, which makes it both hard to get started and almost impossible to get a complete overview over all of its features.
Der mathematische Ratgeber für die ersten beiden Studienjahre!
Wer im Nebenfach Mathematik studieren muß, findet hier das wesentliche mathematische Wissen übersichtlich zusammengestellt und ausführlich erklärt! Viele Beispiele, ein umfangreicher Übungsteil und die konsequente Einbeziehung von WolframAlpha, der freien „Wissensmaschine“ von Wolfram Research, geben Hilfe und Orientierung beim Erlernen der Mathematik an Hochschulen. Abiturienten bei der Vorbereitung auf ein naturwissenschaftlich-technisches, Ingenieur-, Ökonomie- usw.
HL7 for BizTalk provides a detailed guide to the planning and delivery of a HL7-compliant system using the dedicated Microsoft BizTalk for HL7 Accelerator. The HL7 Primary Standard, its various versions, and the use of the HL7 Accelerator for BizTalk are broken out and fully explained. HL7 for BizTalk provides clear guidance on the specific healthcare scenarios that HL7 is designed to overcome and provides working case study models of how HL7 solutions can be implemented in BizTalk, deployed in practice and monitored during operation.
Authors: Wickham, Hadley, Sievert, CarsonBrings the book up-to-date with ggplot2 1.0, including major updates to the theme systemNew scales, stats and geoms added throughoutAdditional practice exercisesA revised introduction that focuses on ggplot() instead of qplot()Updated chapters on data and modeling using tidyr, dplyr and broomThis new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease.
A complete guide for Python programmers to master scientific computing using Python APIs and tools
If you are a Python programmer and want to get your hands on scientific computing, this book is for you. The book expects you to have had exposure to various concepts of Python programming.
In today’s world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers.
If you are an engineer or scientist who wants to create great visualizations with Python, rather than yet another specialized language, this is the book for you. While there are several very competent plotting packages, matplotlib is “just” a Python module.
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.
This book goes beyond a mere description of the different built-in functions coded in the libraries from the SciPy stack. It presents you with a solid mathematical and computational background to help you identify the right tools for each problem in scientific computing and visualization.
Complete Beginner’s Guide To Microsoft Excel – Learn The Basics Of Microsoft Excel In Just 7 Days!Microsoft Excel is a very powerful program if learned and managed correctly. In this book we will learn about his program and what it can do, what you can do with it and the key components that you will use on a daily basis in this program. This book was written for someone who has never used Excel before. I will walk you through the program step by step and give you examples of what you need to do and how to do it.
IPython provides a rich architecture for interactive computing, and as a Python developer you can take advantage of this practical hands-on guide to make yourself an expert. Covers numerical computing, data analysis, and more. You already use Python as a scripting language, but did you know it is also increasingly used for scientific computing and data analysis?
The UNIX Companion contains conceptual information about executing Base SAS in the UNIX operating environment. It contains descriptions of SAS language elements that have behavior specific to UNIX.
Mastering R has never been easierPicking up R can be tough, even for seasoned statisticians and data analysts. "R For Dummies," "2nd Edition" provides a quick and painless way to master all the R you’ll ever need. Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel.
Learn the art of building robust and powerful recommendation engines using RAbout This BookLearn to exploit various data mining techniquesUnderstand some of the most popular recommendation techniquesThis is a step-by-step guide full of real-world examples to help you build and optimize recommendation enginesWho This Book Is ForIf 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.W
SAS users in the Health and Life Sciences industry need to create complex graphs to analyze biostatistics data and clinical data, and they need to submit drugs for approval to the FDA. Graphs used in the HLS industry are complex in nature and require innovative usage of the graphics features. Clinical Graphs Using SAS® provides the knowledge, the code, and real-world examples that enable you to create common clinical graphs using SAS graphics tools, such as the Statistical Graphics procedures and the Graph Template Language.
R is a statistical computing language that’s ideal for answering quantitative finance questions. This book gives you both theory and practice, all in clear language with stacks of real-world examples. Ideal for R beginners or expert alike.
Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists… Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
This book compares the two computer algebra programs, Maple and Mathematica used by students, mathematicians, scientists, and engineers. Structured by presenting both systems in parallel, Mathematica’s users can learn Maple quickly by finding the Maple equivalent to Mathematica functions, and vice versa.
Updated to reflect SAS 9.2, A Handbook of Statistical Analyses using SAS, Third Edition continues to provide a straightforward description of how to conduct various statistical analyses using SAS.
Each chapter shows how to use SAS for a particular type of analysis. The authors cover inference, analysis of variance, regression, generalized linear models, longitudinal data, survival analysis, principal components analysis, factor analysis, cluster analysis, discriminant function analysis, and correspondence analysis.
More than ever before, complicated mathematical procedures are integral to the success and advancement of technology, engineering, and even industrial production. Knowledge of and experience with these procedures is therefore vital to present and future scientists, engineers and technologists.
This book provides an introduction to elementary probability and to Bayesian statistics using de Finetti’s subjectivist approach. One of the features of this approach is that it does not require the introduction of sample space – a non-intrinsic concept that makes the treatment of elementary probability unnecessarily complicate – but introduces as fundamental the concept of random numbers directly related to their interpretation in applications. Events become a particular case of random numbers and probability a particular case of expectation when it is applied to events.
Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks.
Showing 1–24 of 28 results