This article demonstrates a framework for mining relevant entities from a text resume. It shows how separation of parsing logic from entity specification can be achieved. Although only one resume sample is considered here, the framework can be enhanced further to be used not only for different resume formats, but also for documents such as judgments, contracts, patents, medical papers, etc. Majority of world's unstructured data is in the textual form. To make sense of it, one must, either go through it painstakingly or employ certain automated techniques to extract relevant information. Looking at the volume, variety and velocity of such textual data, it is imperative to employ Text Mining techniques to extract the relevant information, transforming unstructured data into structured form, so that further insights, processing, analysis, visualizations are possible.

A heavy maintenance superintendent for a surface coal mine in Elgin, Texas, Evans was responsible for figuring out how to patch or replace outdated parts of a field delivery system that ferried coal from the mine to a plant. Each minute of downtime could cost the company as much as $170. Now the third-generation coal miner gets her adrenaline rush sitting indoors on a soft swivel chair, fixing code on a computer screen. The 33-year-old is a data scientist currently doing a paid residency at Galvanize in Austin. "I was an adrenaline junkie," sad Evans of her past career.

Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business.

That's the thin line Tropico dev Haemimont Games is aiming for with its newly announced city builder Surviving Mars. It's a game about surviving on Mars, if you can believe it. This is no SimCity or Cities: Skylines. Nor is it Tropico, with its comical dictator and his near-infinite powers. The consequences of failure are so much greater here.

AI is one of the buzzwords of the moment in the world of tech, with startups coming at the concept from all angles -- computer vision, machine learning, unstructured data inference and natural language processing being just a handful -- in a wider effort to create more intelligent machines. Now comes a new organization that hopes to find and foster the next wave of AI businesses and products, co-founded by the ex-CEO of Evernote, Phil Libin (pictured above), who has left his role as a managing director at General Catalyst to build it (but he tells me he'll stay on as an advisor). All Turtles, as the new company is called, is not your traditional startup incubator. In an interview with TechCrunch earlier, Libin (whose other co-founders are Jessica Collier (Product Design) and Jon Cifuentes (Research and Operations) described it as "startup studio", more akin to Netflix's push to develop original content than to 500 Startups. It will start out with locations in San Francisco, Tokyo and Paris.

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects -- typically neglected by conventional quantum chemistry approaches -- we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potentials of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the introduction of a fully automated sampling scheme and the use of molecular forces during neural network potential training. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n-alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all these case studies we find excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographics and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms state-of-the-art models significantly.

The boundaries between smart materials, artificial intelligence, embodiment, biology, and robotics are blurring. Smart materials largely cover the same set of physical properties (stiffness, elasticity, viscosity) as biological tissue and state-of-the-art soft robotic technologies that have the potential to deliver this capability. We can foresee smart skins, assist and medical devices, biodegradable and environmental robots or intelligent soft robots. Ultimately wearable assist devices will make conventional assist devices redundant.

Bruce Ratner, The Significant StatisticianTM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients' goals - across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.

Abbott Analytics, provides data mining consulting, knowledge transfer, and training for direct marketing, fraud detection, bioinformatics, and scientific computing. Algoritmica, providing consultancy and customized predictive analytics solutions for a number of international companies. Altius, specializes in the design and building of business-critical information systems that enhance business intelligence (BI) and performance management. Analytica, a consulting and IT firm serving US public and private sector enterprises focused on national security, law enforcement, health care and financial services. Analytics Advisory Group, offers services to improve your business outcomes by providing advisory, consulting, and training services anchored in Analytics. Analytical People offers a range of services, and resources, to any organisations who are looking to deploy Data Mining, Predictive Analytics or Statistical Analysis tools or methods. Anderson Analytics, focuses on helping clients gain the "Information Advantage" via quantitative and qualitative solutions to challenging marketing problems. Anthem Marketing Solutions, marketing and media strategists armed with the analytical capabilities and product solutions you need to deliver on your goals. Apteco, consultation and advice on the use of Faststats data mining to improve business insight and marketing campaigns. ASID Analytics provides data science consulting services, Tulsa, OK, USA. Austin Provider Solution, healthcare and managed care business intelligence solutions, including DSS for Hedis. Bayesia, providing consulting and customized solutions for computer-aided decision making, specializing in Bayesian Networks. Bentley University Center for Quantitative Analysis, provides professional analytical consulting services in support of fundamental and applied business research. Beyond the Arc, Inc., a strategic consultancy specializing in Voice of the Customer; uses analytics and text mining to translate customer data into knowledge, making customer experience more meaningful.