R in Action - early thoughts I was invited to review the book R in Action written by Rob Kabacoff. Since I consider the Quick-R website, created by the same smart guy, one of the most valuable resources about R, It is both an honor and a pleasure to have the opportunity to take an early look at his book and to express some thoughts about it. First, this book is distributed under an early access policy that means, as it is stated on the editor's web site, that: This Early Access version of the book enables you to receive new chapters as they are being written. You can also interact with the authors to ask questions, provide feedback and errata, and help shape the final manuscript on the Author Online. This is a nice publishing approach, the editor settled up an ad-hoc forum which allows real-time feedback from early adopters.
Someone at work recently asked how he should go about studying machine learning on his own. So I’m putting together a little guide. This post will be a living document…I’ll keep adding to it, so please suggest additions and make comments. Fortunately, there’s a ton of great resources that are free and on the web. Guide to Getting Started in Machine Learning
The R programming language for programmers coming from other pro IntroductionAssignment and underscoreVariable name gotchasVectorsSequencesTypesBoolean operatorsListsMatricesMissing values and NaNsCommentsFunctionsScopeMisc.Other resources Ukrainian translation Other languages:
Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). This manual provides information on data types, programming elements, statistical modelling and graphics. An Introduction to R
Acquisition Supports Via Science’s Move to Leverage Proprietary Machine Learning Platform, REFS™, Beyond Healthcare and Financial Services Sectors Cambridge, Mass. – March 4, 2011 – Via Science announced the acquisition of Dataspora, a predictive analytics firm that helps companies solve complex big data problems. The acquisition helps strengthen Via’s positioning to support the consumer packaged goods and retail sectors, areas of focus for Dataspora. REFS™ provides the ability to leverage causal mathematics at scale with its supercomputing platform. This allows decision-makers to make better use of data with mathematical models that can diagnose problems or predict future outcomes. Via has invested over 10 years and $25 million to prove the value of REFS™ in high-stakes problem areas such as precision medicine and quantitative trading. How Google and Facebook are using R
The R Project for Statistical Computing