Search results for Data Science

Found 4780 documents, 5946 searched:

• Using Deep Learning To Extract Knowledge From Job Descriptions">Using Deep Learning To Extract Knowledge From Job Descriptions

  We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.

  https://www.kdnuggets.com/2017/05/deep-learning-extract-knowledge-job-descriptions.html

• Do We Need Balanced Sampling?

  Resampling is a solution which is very popular in dealing with class imbalance. Our research on churn prediction shows that balanced sampling is unnecessary.

  https://www.kdnuggets.com/2017/05/need-balanced-sampling.html

• Dask and Pandas and XGBoost: Playing nicely between distributed systems

  This blogpost gives a quick example using Dask.dataframe to do distributed Pandas data wrangling, then using a new dask-xgboost package to setup an XGBoost cluster inside the Dask cluster and perform the handoff.

  https://www.kdnuggets.com/2017/04/dask-pandas-xgboost-playing-nicely-distributed-systems.html

• E-learning courses on Advanced Analytics, Credit Risk Modeling, and Fraud Analytics

  These online courses, developed by Prof. Bart Baesens and SAS, include videos, case studies, quizzes, and focus on focusses on the concepts and modeling methodologies and not on specific software.

  https://www.kdnuggets.com/2017/04/sas-elearning-advanced-analytics-credit-risk-modeling-fraud.html

• A Brief History of Artificial Intelligence">A Brief History of Artificial Intelligence

  This post is a brief outline of what happened in artificial intelligence in the last 60 years. A great place to start or brush up on your history.
   
   

  https://www.kdnuggets.com/2017/04/brief-history-artificial-intelligence.html

• Stuff Happens: A Statistical Guide to the “Impossible”

  Why are some people struck by lightning multiple times or, more encouragingly, how could anyone possibly win the lottery more than once? The odds against these sorts of things are enormous.

  https://www.kdnuggets.com/2017/04/stuff-happens-statistical-guide-impossible.html

• Top 20 Recent Research Papers on Machine Learning and Deep Learning">Top 20 Recent Research Papers on Machine Learning and Deep Learning

  Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".

  https://www.kdnuggets.com/2017/04/top-20-papers-machine-learning.html

• Putting Together A Full-Blooded AI Maturity Model

  Here is a proposed “7A” model that is useful enough to capture of the core of what AI offers without falsely implying there is a static body of best practices in this area.

  https://www.kdnuggets.com/2017/04/ai-maturity-model.html

• What is AI? Ingredients for Intelligence

  This introductory overview of artificial intelligence acts as a layman's guide what AI is, and what it is made up of.

  https://www.kdnuggets.com/2017/04/grakn-artificial-intelligence-ingredients-intelligence.html

• What is Structural Equation Modeling?">What is Structural Equation Modeling?

  Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?

  https://www.kdnuggets.com/2017/03/structural-equation-modeling.html

• Unsupervised Investments: A Comprehensive Guide to AI Investors

  This article presents a list of 80 funds investing in Artificial Intelligence and Machine Learning.

  https://www.kdnuggets.com/2017/03/unsupervised-investments-guide-ai-investors.html

• Homebrewed Deep Learning and Do-It-Yourself Robotics

  Learn how to experiment with embodied robotic cognition with IBM Project Intu, a platform that extends Deep Learning and other cognitive services to new devices with minimum coding.

  https://www.kdnuggets.com/2017/03/ibm-homebrewed-deep-learning-robotics.html

• Text Analytics: A Primer

  Marketing scientist Kevin Gray asks Professor Bing Liu to give us a quick snapshot of text analytics in this informative interview.

  https://www.kdnuggets.com/2017/03/text-analytics-primer.html

• K-Means & Other Clustering Algorithms: A Quick Intro with Python

  In this intro cluster analysis tutorial, we'll check out a few algorithms in Python so you can get a basic understanding of the fundamentals of clustering on a real dataset.

  https://www.kdnuggets.com/2017/03/k-means-clustering-algorithms-intro-python.html

• What is Customer Churn Modeling? Why is it valuable?

  Getting new customers is much more more expensive than retaining existing ones, so reducing churn is a top priority for many firms. Understanding why customers churn and estimating the risks are powerful components of a data-driven retention strategy.

  https://www.kdnuggets.com/2017/03/datascience-customer-churn-modeling.html

• 7 More Steps to Mastering Machine Learning With Python">7 More Steps to Mastering Machine Learning With Python

  This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
   
   

  https://www.kdnuggets.com/2017/03/seven-more-steps-machine-learning-python.html

• The Anatomy of Deep Learning Frameworks">The Anatomy of Deep Learning Frameworks

  This post sketches out some common principles which would help you better understand deep learning frameworks, and provides a guide on how to implement your own deep learning framework as well.
   

  https://www.kdnuggets.com/2017/02/anatomy-deep-learning-frameworks.html

• Introduction to Correlation

  Correlation is one of the most widely used (and widely misunderstood) statistical concepts. We provide the definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library.

  https://www.kdnuggets.com/2017/02/datascience-introduction-correlation.html

• Fixing Deployment and Iteration Problems in CRISP-DM

  Many analytic models are not deployed effectively into production while others are not maintained or updated. Applying decision modeling and decision management technology within CRISP-DM addresses this.

  https://www.kdnuggets.com/2017/02/fixing-deployment-iteration-problems-crisp-dm.html

• New e-learning course: Fraud Analytics using Descriptive, Predictive and Social Network Analytics

  This online course teaches how to find fraud patterns from historical data using descriptive analytics, and social network learning.

  https://www.kdnuggets.com/2017/01/sas-elearning-course-fraud-analytics-social-network-analytics.html

• Deep Learning Research Review: Natural Language Processing">Deep Learning Research Review: Natural Language Processing

  This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.

  https://www.kdnuggets.com/2017/01/deep-learning-review-natural-language-processing.html

• 6 areas of AI and Machine Learning to watch closely">6 areas of AI and Machine Learning to watch closely

  Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

  https://www.kdnuggets.com/2017/01/6-areas-ai-machine-learning.html

• Introduction to Forecasting with ARIMA in R

  ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.

  https://www.kdnuggets.com/2017/01/datascience-introduction-forecasting-arima-r.html

• Deep Learning Can be Applied to Natural Language Processing">Deep Learning Can be Applied to Natural Language Processing

  This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.

  https://www.kdnuggets.com/2017/01/deep-learning-applied-natural-language-processing.html

• Top KDnuggets tweets, Jan 04-10: Cartoon: When Self-Driving Car takes you too far; A massive collection of free programming books

  Also AI #DataScience #MachineLearning: Main Developments 2016, Key Trends 2017; Scikit-Learn Cheat Sheet: #Python #MachineLearning

  https://www.kdnuggets.com/2017/01/top-tweets-jan04-10.html

• Social Media for Marketing and Healthcare: Focus on Adverse Side Effects

  Social media like twitter, facebook are very important sources of big data on the internet and using text mining, valuable insights about a product or service can be found to help marketing teams. Lets see, how healthcare companies are using big data and text mining to improve their marketing strategies.

  https://www.kdnuggets.com/2017/01/social-media-marketing-healthcare-focus-adverse-side-effects.html

• Ten Myths About Machine Learning, by Pedro Domingos

  Myths on artificial intelligence and machine learning abound. Noted expert Pedro Domingos identifies and refutes a number of these myths, of both the pessimistic and optimistic variety.

  https://www.kdnuggets.com/2017/01/domingos-ten-myths-machine-learning.html

• ResNets, HighwayNets, and DenseNets, Oh My!

  This post walks through the logic behind three recent deep learning architectures: ResNet, HighwayNet, and DenseNet. Each make it more possible to successfully trainable deep networks by overcoming the limitations of traditional network design.

  https://www.kdnuggets.com/2016/12/resnets-highwaynets-densenets-oh-my.html

• Introduction to Bayesian Inference

  Bayesian inference is a powerful toolbox for modeling uncertainty, combining researcher understanding of a problem with data, and providing a quantitative measure of how plausible various facts are. This overview from Datascience.com introduces Bayesian probability and inference in an intuitive way, and provides examples in Python to help get you started.

  https://www.kdnuggets.com/2016/12/datascience-introduction-bayesian-inference.html

• Artificial Intelligence and Life in 2030

  Read this engaging overview of a report from the Stanford University 100 year study of Artificial Intelligence, “a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society.”

  https://www.kdnuggets.com/2016/12/artificial-intelligence-life-2030.html

• Introduction to K-means Clustering: A Tutorial

  A beginner introduction to the widely-used K-means clustering algorithm, using a delivery fleet data example in Python.

  https://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html

• Deep Learning Research Review: Reinforcement Learning

  This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.

  https://www.kdnuggets.com/2016/11/deep-learning-research-review-reinforcement-learning.html

• Artificial Intelligence, Deep Learning, and Neural Networks, Explained">Artificial Intelligence, Deep Learning, and Neural Networks, Explained

  This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

  https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html

• 9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained

  If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.

  https://www.kdnuggets.com/2016/09/9-key-deep-learning-papers-explained.html

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

  This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.

  https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1">A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

  Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

  https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-1.html

• MDL Clustering: Unsupervised Attribute Ranking, Discretization, and Clustering

  MDL Clustering is a free software suite for unsupervised attribute ranking, discretization, and clustering based on the Minimum Description Length principle and built on the Weka Data Mining platform.

  https://www.kdnuggets.com/2016/08/mdl-clustering-unsupervised-attribute-ranking-discretization-clustering.html

• Top Machine Learning Projects for Julia

  Julia is gaining traction as a legitimate alternative programming language for analytics tasks. Learn more about these 5 machine learning related projects.

  https://www.kdnuggets.com/2016/08/top-machine-learning-projects-julia.html

• Predictive Analytics Introductory Key Terms, Explained

  Here is a collection of introductory predictive analytics terms and concepts, presented for the newcomer in a straight-forward, no frills definition style.

  https://www.kdnuggets.com/2016/07/siegel-predictive-analytics-key-terms-explained.html

• Deep Residual Networks for Image Classification with Python + NumPy

  This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.

  https://www.kdnuggets.com/2016/07/deep-residual-neworks-image-classification-python-numpy.html

• Recursive (not Recurrent!) Neural Networks in TensorFlow

  Learn how to implement recursive neural networks in TensorFlow, which can be used to learn tree-like structures, or directed acyclic graphs.

  https://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html

• Top Machine Learning Libraries for Javascript

  Javascript may not be the conventional choice for machine learning, but there is no reason it cannot be used for such tasks. Here are the top libraries to facilitate machine learning in Javascript.

  https://www.kdnuggets.com/2016/06/top-machine-learning-libraries-javascript.html

• Ten Simple Rules for Effective Statistical Practice: An Overview

  An overview of 10 simple rules to follow to ensure proper effective statistical data analysis.

  https://www.kdnuggets.com/2016/06/ten-simple-rules-effective-statistical-practice-overview.html

• New Andrew Ng Machine Learning Book Under Construction, Free Draft Chapters

  Check out the details on Andrew Ng's new book on building machine learning systems, and find out how to get your free copy of draft chapters as they are written.

  https://www.kdnuggets.com/2016/06/free-machine-learning-book-draft-chapters.html

• Apache Spark Key Terms, Explained

  An overview of 13 core Apache Spark concepts, presented with focus and clarity in mind. A great beginner's overview of essential Spark terminology.

  https://www.kdnuggets.com/2016/06/spark-key-terms-explained.html

• Deep Learning for Chatbots, Part 1 – Introduction

  The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.

  https://www.kdnuggets.com/2016/04/deep-learning-chatbots-part-1.html

• Recommender Systems: New Comprehensive Textbook by Charu Aggarwal

  Covers recommender systems comprehensively, both fundamentals and advanced topics, organized into: Algorithms and evaluation, recommendations in specific domains and contexts, and advanced topics and applications.

  https://www.kdnuggets.com/2016/04/recommender-systems-textbook.html

• Regression & Correlation for Military Promotion: A Tutorial

  A clear and well-written tutorial covering the concepts of regression and correlation, focusing on military commander promotion as a use case.

  https://www.kdnuggets.com/2016/04/regression-correlation-military-tutorial.html

• Must Know Tips for Deep Learning Neural Networks

  Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.

  https://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.html

• A comparison between PCA and hierarchical clustering

  Graphical representations of high-dimensional data sets are the backbone of exploratory data analysis. We examine 2 of the most commonly used methods: heatmaps combined with hierarchical clustering and principal component analysis (PCA).

  https://www.kdnuggets.com/2016/02/qlucore-comparison-pca-hierarchical-clustering.html

• How Small is the World, Really?

  Social network analysis is back in the news again, with a recent Facebook project which determined that there are an average of 3.5 intermediaries between any 2 Facebook users. But this is different than "6 degrees of separation." Read on to find out why, and how.

  https://www.kdnuggets.com/2016/02/how-small-is-world-really.html

• Opening Up Deep Learning For Everyone

  Opening deep learning up to everyone is a noble goal. But is it achievable? Should non-programmers and even non-technical people be able to implement deep neural models?

  https://www.kdnuggets.com/2016/02/opening-deep-learning-everyone.html

• Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn">Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn

  Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. Does it succeed in making deep learning more accessible?

  https://www.kdnuggets.com/2016/02/scikit-flow-easy-deep-learning-tensorflow-scikit-learn.html

• AI Supercomputers: Microsoft Oxford, IBM Watson, Google DeepMind, Baidu Minwa

  In the world of AI, this is the equivalent of the US and USSR competing to put their guy on the moon first. Here is a profile of some of the giants locked into the AI space race.

  https://www.kdnuggets.com/2016/02/ai-supercomputers-microsoft-ibm-watson-google-deepmind-baidu.html

• Is Deep Learning Overhyped?

  With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?

  https://www.kdnuggets.com/2016/01/deep-learning-overhyped.html

• Free Online Course: Statistical Learning

  With a free MOOC from Stanford, dive into statistical learning with the respected professors who literally wrote the book on it.

  https://www.kdnuggets.com/2016/01/course-stanford-statistical-learning.html

• Free Book Download: Statistical Learning with Sparsity: The Lasso and Generalizations

  We witness an explosion of Big Data in finance, biology, medicine, marketing, and other fields. This book describes the important statistical ideas for learning from large and sparse data in a common conceptual framework.

  https://www.kdnuggets.com/2016/01/book-statistical-learning-sparsity-lasso-generalizations.html

• 50 Deep Learning Software Tools and Platforms, Updated

  We present the popular software & toolkit resources for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. Explore the new list!

  https://www.kdnuggets.com/2015/12/deep-learning-tools.html

• Top KDnuggets tweets, Nov 16-22: Dilbert discovers the perfect chart; TensorFlow Disappoints – Google Deep Learning falls shallow

  A standard #graph for any occasion! #Dilbert discovers the perfect chart; TensorFlow Disappoints - Google #DeepLearning falls shallow; All the #BigData tools and how to use them; KDnuggets #DataScience #Cartoon Caption Contest.

  https://www.kdnuggets.com/2015/11/top-tweets-nov16-22.html

• Deep Learning for Visual Question Answering

  Here we discuss about the Visual Question Answering problem, and I’ll also present neural network based approaches for same.

  https://www.kdnuggets.com/2015/11/deep-learning-visual-question-answering.html

• Why Deep Learning Works – Key Insights and Saddle Points

  A quality discussion on the theoretical motivations for deep learning, including distributed representation, deep architecture, and the easily escapable saddle point.

  https://www.kdnuggets.com/2015/11/theoretical-deep-learning.html

• Top /r/MachineLearning Posts, September: Implement a neural network from scratch in C++

  Neural network in C++ for beginners, Chinese character handwriting recognition beats humans, a handy machine learning algorithm cheat sheet, neural nets versus functional programming, and a neural nets paper repository.

  https://www.kdnuggets.com/2015/10/top-reddit-machine-learning-september.html

• 11 things to know about Sentiment Analysis

  Seth Grimes, a text analytics guru, shares 11 key observations on what works, what is past, what is coming, and what to keep in mind while doing sentiment analysis.

  https://www.kdnuggets.com/2015/08/11-things-about-sentiment-analysis.html

• Top 30 Social Network Analysis and Visualization Tools

  We review major tools and packages for Social Network Analysis and visualization, which have wide applications including biology, finance, sociology, network theory, and many other domains.

  https://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.html

• Algorithmia: Building a web site explorer in 5 easy steps

  We show how to use Algorithmia for quickly building a functional web site explorer in 5 steps: GetLinks, PageRank, Url2text, Summarizer and AutoTag.

  https://www.kdnuggets.com/2015/04/algorithmia-building-web-site-explorer-5-easy-steps.html

• Interview: Ksenija Draskovic, Verizon on Dissecting the Anatomy of Predictive Analytics Projects

  We discuss Predictive Analytics use cases at Verizon Wireless, advantages of a unified data view, model selection and common causes of failure.

  https://www.kdnuggets.com/2015/04/interview-ksenija-draskovic-verizon-predictive-analytics.html

• Talking Machine – 3 Deep Learning Gurus Talk about History and Future of Machine Learning, part 1

  An recent interview from the talking machine podcast with three deep learning experts. They talked about the neural network winter and its renewal.

  https://www.kdnuggets.com/2015/03/talking-machine-deep-learning-gurus-p1.html

• Interview: Brad Klingenberg, StitchFix on Building Analytics-powered Personal Stylist

  We discuss StitchFix, how it leverages Analytics, understanding customer preferences, and pros-and-cons of involving human judgement in the recommendation process.

  https://www.kdnuggets.com/2015/03/interview-brad-klingenberg-stitchfix-analytics.html

• Deep Learning, The Curse of Dimensionality, and Autoencoders

  Autoencoders are an extremely exciting new approach to unsupervised learning and for many machine learning tasks they have already surpassed the decades of progress made by researchers handpicking features.

  https://www.kdnuggets.com/2015/03/deep-learning-curse-dimensionality-autoencoders.html

• 7 common mistakes when doing Machine Learning

  In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.

  https://www.kdnuggets.com/2015/03/machine-learning-data-science-common-mistakes.html

• Interview: David Kasik, Boeing on How Visual Analytics is Improving Aviation Safety

  We discuss data visualization at Boeing, the importance of Visual Analytics, Aviation Safety improvement through Analytics and augmented reality.

  https://www.kdnuggets.com/2015/02/interview-david-kasik-boeing-visual-analytics-aviation.html

• (Deep Learning’s Deep Flaws)’s Deep Flaws

  Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.

  https://www.kdnuggets.com/2015/01/deep-learning-flaws-universal-machine-learning.html

• The High Cost of Maintaining Machine Learning Systems

  Google researchers warn of the massive ongoing costs for maintaining machine learning systems. We examine how to minimize the technical debt.

  https://www.kdnuggets.com/2015/01/high-cost-machine-learning-technical-debt.html

• Interview: Paul Robbins, STATS on the Potential and Challenges for Sports Analytics

  We discuss Analytics at STATS, typical daily tasks, ICE Analytics platform, key challenges, response from coaches/players, career advice and more.

  https://www.kdnuggets.com/2015/01/interview-paul-robbins-stats-sports-analytics.html

• Geoff Hinton AMA: Neural Networks, the Brain, and Machine Learning

  In a wide-ranging Q&A, Geoff Hinton addresses the future of deep learning, its biological inspirations, and his research philosophy.

  https://www.kdnuggets.com/2014/12/geoff-hinton-ama-neural-networks-brain-machine-learning.html

• TweetNLP: Twitter Natural Language Processing

  A short overview of Natural Language Processing tools and utilities developed by Prof. Noah Smith, CMU and his team to analyze Twitter data.

  https://www.kdnuggets.com/2014/10/tweetnlp-twitter-natural-language-processing.html

• Perfume, computer programming, and Harvard

  What is the connection between Perfume, computer programming, and Harvard education? Peter Bruce explains.

  https://www.kdnuggets.com/2014/10/perfume-computer-programming-harvard.html

• Most Viewed Web Mining Lectures

  Discover interesting lectures on topics like mining information networks and identifying influential members of online communities in this list of the top viewed web mining lectures on videolectures.net.

  https://www.kdnuggets.com/2014/09/most-viewed-web-mining-lectures-videolectures.html

• Most Viewed Machine Learning Talks at Videolectures

  Discover lectures from a variety of summer schools and conference tutorials on machine learning in this list of the top lectures on the subject from videolectures.net.

  https://www.kdnuggets.com/2014/09/most-viewed-machine-learning-talks-videolectures.html

• Deep Learning – important resources for learning and understanding

  New and fundamental resources for learning about Deep Learning - the hottest machine learning method, which is approaching human performance level.

  https://www.kdnuggets.com/2014/08/deep-learning-important-resources-learning-understanding.html

• Containers: The Enabler of YARN

  The evolution of a data-center operating system is discussed along with the underlying challenges and approaches being followed. Containers play a big role in enabling the required abstraction and deliver additional benefits.

  https://www.kdnuggets.com/2014/07/containers-yarn-enabler.html

• The Algorithm that Runs the World Can Now Run More of It

  The most important algorithm, used for optimizing almost everything, is linear programming. New advances allow linear programming problems to be solved faster using the new commercial parallel simplex solver.

  https://www.kdnuggets.com/2014/06/fico-algorithm-that-runs-world.html

• OpenNN, An Open Source Library For Neural Networks

  OpenNN is an open source class library written in C++ which implements neural networks, and runs on Windows, Apple, or Linux.

  https://www.kdnuggets.com/2014/06/opennn-open-source-library-neural-networks.html

• New Book: Social Media Mining – free PDF download

  Social Media Mining integrates social media, social network analysis, and data mining to enable students, practitioners, researchers, and managers to understand the basics and potentials of this field.

  https://www.kdnuggets.com/2014/04/book-social-media-mining-free-download.html

• Evolution of Fraud Analytics – An Inside Story

  The amazing analytic innovations in payment fraud prevention can be grouped into three major categories: large data-set modeling, sparse data-set modeling, and false-positive reductions - a view from the inside.

  https://www.kdnuggets.com/2014/03/evolution-fraud-analytics-inside-story.html

• Qualitative Analytics: Why numbers do not tell the complete story?

  Data scientists love numbers, yet not all data is numerical. Qualitative analytics should not be ignored, especially given the unique value it provides.

  https://www.kdnuggets.com/2014/02/qualitative-analysis-why-numbers-dont-tell-complete-story.html

• Statistics Software

  commercial | free Analyse-it!, accurate low-cost statistical software for Microsoft Excel. Appricon's Analysis Studio, a statistical analysis and modeling software with advanced logistic regression modeling, Read more »

  https://www.kdnuggets.com/software/statistics.html

• Graph and Social Network Analysis, Link Analysis, and Visualization

  commercial software | sites | free software AdvancedMiner Social Network Analysis (SNA), models social relationships among persons, designed to enhance the available customer information with Read more »

  https://www.kdnuggets.com/software/social-network-analysis.html

• Clustering and Segmentation Software

  Commercial Clustering Software BayesiaLab, includes Bayesian classification algorithms for data segmentation and uses Bayesian networks to automatically cluster the variables. ClustanGraphics3, hierarchical cluster analysis from Read more »

  https://www.kdnuggets.com/software/clustering.html

• What is PMML?

  Alex Guazzelli (VP, Analytics at Zementis), answers: PMML stands for "Predictive Model Markup Language". It is the de facto standard to represent predictive solutions. A Read more »

  https://www.kdnuggets.com/faq/pmml.html

• Statistics Sites

  American Statistical Association, a scientific and educational society founded in 1839 to foster excellence in the use and application of statistics to the biological, physical, Read more »

  https://www.kdnuggets.com/websites/statistics.html

• Bioinformatics Companies

  A B C D E F G H IJ K L M N O P Q R S T U V W XYZ A2IDEA is Read more »

  https://www.kdnuggets.com/companies/bioinformatics.html

• Statistical Golden Rule

  Bruce Ratner examines how to combine skills acquired by experience (art) and a technique that reflects a precise application of fact or principle (science).

  https://www.kdnuggets.com/2013/12/statistical-golden-rule.html

• Top 10 Big Ideas in Harvard Statistics 110 Class

  The Big Ideas in Statistics include: Conditioning (the soul of statistics), Random variables and random vectors, Stories, Symmetry, Linearity of expectation, LOTUS, Variance, covariance, and correlation.

  https://www.kdnuggets.com/2013/12/top-10-big-ideas-harvard-statistics-110-class.html

• Stock Analysis and Prediction Solutions

  Alyuda NeuroSignal XL, neural network Excel add-in for stock predictions and trading systems testing. BioComp Profit Neural Network, reports 150-200% returns trading the S&P500/E-Mini. DeepInsight, Read more »

  https://www.kdnuggets.com/solutions/stocks.html

• Bioinformatics Solutions

  A B C D E F G H I J K L M N O P Q R S T U V W XYZ ADMEWORKS Read more »

  https://www.kdnuggets.com/solutions/bioinformatics.html

• PMML FAQ: Predictive Model Markup Language

  An update on PMML (Predictive Model Markup Language), de facto standard to represent predictive solutions. With PMML 4.1, all the capabilities available for data pre-processing were also made available for post-processing.

  https://www.kdnuggets.com/2013/01/pmml-faq-predictive-model-markup-language.html

• KDnuggets Exclusive: Interview with Rayid Ghani, Chief Scientist Obama 2012 Campaign

  Rayid Ghani was a leading analytics professional before he joined Obama reelection campaign as a Chief Scientist. KDnuggets asks him about analytics role in the campaign, key to success, Big Data bubble, and advice for aspiring data scientists.

  https://www.kdnuggets.com/2013/01/kdnuggets-exclusive-interview-rayid-ghani-chief-scientist-obama-2012-campaign.html

• muPDNA: Mu Sigma First Analytics Software Product

  Mu Sigma first analytics software product, muPDNA™ offers a structured approach to defining, representing and encoding intelligence about business problems. Originally developed for internal use, muPDNA is now available to any enterprise.

  https://www.kdnuggets.com/2013/03/mupdna-mu-sigma-first-analytics-software-product.html

• Decision Tree Software for Classification

  commercial | free AC2, provides graphical tools for data preparation and builing decision trees. Alice d'Isoft 6.0, a streamlined version of ISoft's decision-tree-based AC2 data-mining Read more »

  https://www.kdnuggets.com/software/classification-decision-tree.html