Tag: Classification

Feed: R-bloggers. Author: R Views. Andrie de Vries is the author of “R for Dummies” and a Solutions Engineer at RStudio Phillip (Armand) Bester is a medical scientist, researcher, and lecturer at the Division of Virology, University of the Free State, and National Health Laboratory Service (NHLS), Bloemfontein, South Africa In this post we complete our series on analysing the HIV pandemic in Africa. Previously we covered the bigger picture of HIV infection in Africa, and a pipeline for drug resistance testing of samples in the lab. Then, in part 3 we saw that sometimes the same patient’s genotype must ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Capri Granville. A free online version of the second edition of the book based on Stat 110, Introduction to Probability by Joe Blitzstein and Jessica Hwang, is now available here. Print copies are available via CRC Press, Amazon, and elsewhere.  Stat110x is also available as an free edX course, here.  The edX course focuses on animations, interactive features, readings, and problem-solving, and is complementary to the Stat 110 lecture videos on YouTube, which are available here. The Stat110x animations are available within the course and here. For more information, visit Stat110 at Harvard. A 10-page cheat sheet summarizing the content, is ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: melvin-greer; Every sector in the world is currently investigating Artificial intelligence (AI). Groundbreaking advancements in AI will take place in health and life sciences, cyber-intelligence, smart cities and transportation.But before anyone can execute an AI strategy, they’ll need a data strategy. The data strategy behind AI When we think about the vast majority of the work that data scientists do, 85 percent of that work is associated with data governance, cleansing, tagging and classification of data—all elements that are embodied in the concept of a data strategy. Thus, harnessing ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: dan-sutherland; A modern data architecture (MDA) must support the next generation cognitive enterprise which is characterized by the ability to fully exploit data using exponential technologies like pervasive artificial intelligence (AI), automation, Internet of Things (IoT) and blockchain. In addition, an MDA must support a platform-centric business model that fully supports people, process and technology and is optimized around business goals.These goals are admirable but difficult. In today’s rapidly-changing landscape, it is difficult to keep up with the latest technologies – AWS alone released over 1,800 new services and ... Read More

Feed: SAS Blogs. Author: Patricia Neri. This blog shows how the automatically generated concepts and categories in Visual Text Analytics (VTA) can be refined using LITI and Boolean rules. Because of these capabilities highly customized models can be developed in VTA. The rules used in this blog are basic. Developing linguistic rules and accurately categorizing documents requires subject matter expertise and understanding the grammatical structure of the language(s) used. I will use a data set that contains information on 1527 randomly selected movies: their titles, reviews, MPAA Ratings, Main Genre classifications and Viewer Ratings. Two customized categories will be developed ... Read More

Feed: AWS Big Data Blog. Notebooks are increasingly becoming the standard tool for interactively developing big data applications. It’s easy to see why. Their flexible architecture allows you to experiment with data in multiple languages, test code interactively, and visualize large datasets. To help scientists and developers easily access notebook tools, we launched Amazon EMR Notebooks, a managed notebook environment that is based on the popular open-source Jupyter notebook application. EMR Notebooks support Spark Magic kernels, which allows you to submit jobs remotely on your EMR cluster using languages like PySpark, Spark SQL, Spark R, and Scala. The kernels submit ... Read More

Feed: R-bloggers. Author: rOpenSci - open tools for open science. We strive for high quality in our suite of packages, in practice via a system of software peer review, and via packaging guidelines that keep growing. There is therefore a risk of increasing the workload of package authors, who already have a lot on their plate. To avoid that, when explaining how to do things in our dev guide, we recommend existing automated tools to authors. The usethis package implements this important principle: Automate that which can be automated. Your computer was literally born to implement rote-but-fussy stuff for you ... Read More

Feed: SAS Blogs. Author: Patricia Neri. In this blog, I use a Recurrent Neural Network (RNN) to predict whether opinions for a given review will be positive or negative. This prediction is treated as a text classification example. The Sentiment Classification Model is trained using deepRNN algorithms and the resulting model is used to predict if new reviews are positive or negative. I provide two versions of the code: one in CASL and the other one in Python but both will call the same deepRNN algorithms executed by CAS. Machine learning is embedded in many SAS algorithms and offerings. In ... Read More

Feed: R-bloggers. Author: Learning Machines. There are a million reasons to learn R (see e.g. Why R for data science – and not Python?), but where to start? I present to you the ultimate introduction to bring you up to speed! So read on… I call it ultimate because it is the essence of many years of teaching R… or put differently: it is the kind of introduction I would have liked to have when I started out with R back in the days! A word of warning though: this is a introduction to R and not to statistics, so ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques. By now everyone is at least familiar with using AI/ML as a required cornerstone of company strategy.  Frequently this is referred to as ‘digitization’ or the ‘digital journey’.  There’s plenty of data showing that early adopters who have gone all-in on this approach are already pulling ahead of competitors both in share and bottom line results. There’s also mounting ... Read More