Tag: Classification

Feed: Featured Posts - DataViz Author: Andrei Macsin Guest blog post by Irina Papuc Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This tutorial ... Read More

Feed: Featured Blog Posts - Data Science Central Author: Emmanuelle Rieuf Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing ... Read More

Feed: Featured Blog Posts - Data Science Central Author: William Vorhies Summary:  Which of these terms means the same thing:  AI, Deep Learning, Machine Learning?  Are you sure?  While there’s overlap none of these is a complete subset of the others and none completely explains the others. Take this quiz. Which of the following are substantially the same things? A. AI B. Deep Learning C.  Machine Learning (Select your answer) 1.  A and B 2.  B and C 3.  A and C 4.  All of the above. For as precise a profession as we data scientists purport to be we ... Read More

Feed: Resources Discussions - Data Science Central Author: Vincent Granville The following is a selection of featured articles that were posted in our previous weekly digests, in short, the best of the best on DSC. Single-starred articles are written by external/guest bloggers. Older popular articles are being added regularly, so please check out this page once a week! Our upcoming book on data science 2.0 (or data science automation or data science handbook or the little data science book, not sure yet about the title) will be based on some of these (edited and revised) articles: these articles are double-starred ... Read More

Feed: Resources Discussions - Data Science Central Author: Vincent Granville Here is selection containing both external and internal papers, focusing on various technical aspects of data science and big data. Feel free to add your favorites. Complex Open Text Analysis: Source: Avinash Kaushik External Papers Bigtable: A Distributed Storage System for Structured Data A Few Useful Things to Know about Machine Learning Random Forests A Relational Model of Data for Large Shared Data Banks Map-Reduce for Machine Learning on Multicore Pasting Small Votes for Classification in Large Databases and On-Line Recommendations Item-to-Item Collaborative Filtering Recursive Deep Models for Semantic Compositionality ... Read More

Feed: Featured Blog Posts - Data Science Central Author: William Vorhies Summary:  The most important developments in Deep Learning and AI in the last year may not be technical at all, but rather a major change in business model.  In the space of about six months all the majors have made their Deep Learning IP open source, hoping to gain on the competition from the power of the broader developer base and wide adoption.   To say that the last year has been big for Deep Learning is an understatement.  There have been some spectacular technical innovations like Microsoft winning ... Read More

Feed: Featured Blog Posts - Big Data News Author: suresh kumar gorakala As a part of Twitter Data Analysis, So far I have completed Movie review using R& Document Classification using R. Today we will be dealing with discovering topics in Tweets, i.e. to mine the tweets data to discover underlying topics– approach known as Topic Modeling. What is Topic Modeling? A statistical approach for discovering “abstracts/topics” from a collection of text documents based on statistics of each word. In simple terms, the process of looking into a large collection of documents, identifying clusters of words and grouping them together based on similarity ... Read More

Feed: Analytics Courses Discussions - AnalyticBridge Author: Vincent Granville Submitted by Sudhanshu Ahuja. Image source: Link Which top Masters Courses should you consider for a great career in data-science? A frequently cited study by McKinsey predicts that by 2018, the United States could face a shortage of 140,000 to 190,000 "people with deep analytic skills" as well as 1.5 million "managers and analysts with the know-how to use the analysis of big data to make effective decisions." The field is so hot right now that Roy Lowrance, the managing director of New York University's new Center for Data Science program says "Anything ... Read More

Feed: Data Science Research Discussions - Data Science Central Author: Vincent Granville Originally posted on DataScienceCentral, by Dr. Granville. Click here to read original article and comments. In this article, I proposes a simple metric to measure predictive power. It is used for combinatorial feature selection, where a large number of feature combinations need to be ranked automatically and very fast, for instance in the context of transaction scoring, in order to optimize predictive models. This is about rather big data, and we would like to see an Hadoop methodology for the technology proposed here. It can easily be implemented in a Map Reduce ... Read More

Feed: Featured Posts - Hadoop360 Author: Andrei Macsin Originally posted on Data Science Central This article introduces Mahout, a library for scalable machine learning, and studies potential applications through two Mahout projects. It was written by Linda Terlouw. Linda is a computer scientist who works on Data Science (Data Analysis, Data Visualization, Process Mining). Apache Mahout is a library for scalable machine learning. Originally a subproject of Apache Lucene (a high-performance text search engine library), Mahout has progressed to be a top-level Apache project.  While Mahout has only been around for a few years, it has established itself as a frontrunner in the field of ... Read More