Data mining, text mining, natural language processing, and computational linguistics: some definitions

#artificialintelligence

Every once in a while an innocuous technical term suddenly enters public discourse with a bizarrely negative connotation. I first noticed the phenomenon some years ago, when I saw a Republican politician accusing Hillary Clinton of "parsing." From the disgust with which he said it, he clearly seemed to feel that parsing was morally equivalent to puppy-drowning. It seemed quite odd to me, since I'd only ever heard the word "parse" used to refer to the computer analysis of sentence structures. The most recent word to suddenly find itself stigmatized by Republicans (yes, it does somehow always seem to be Republican politicians who are involved in this particular kind of linguistic bullshittery) is "encryption."


Anomaly Detection with Azure Machine Learning Studio. TechBullion

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We knew Azure is one of the fastest growing Cloud services around the world it helps developers and IT Professionals to create and manage their applications. When Azure HDInsight has huge success in Hadoop based technology, For Marketing Leaders in Big data Microsoft has taken another step and introduced Azure Machine Learning which is also called as "Azure ML". After the release of Azure ML, the developers feel easy to build applications and Azure ML run's under a public cloud by this user need not to download any external hardware or software. Azure Machine Learning is combined in the development environment which is renamed as Azure ML Studio. The main reason to introduce Azure ML to make users to create a data models without the help of data science background, In Azure ML Data models, are Created with end-to-end services were as ML Studio is used to build and test by using drag-and-drop and also we can deploy analytics solution for our data's too.


A simple approach to anomaly detection in periodic big data streams

@machinelearnbot

One of the signature traits of big data is that large volumes are created in short periods of time. This data often comes from connected devices, such as mobile phones, vehicle fleets or industrial machinery. The reasons for generating and observing this data are many, yet a common problem is the detection of anomalous behaviour. This may be a machine in a factory that is on the verge of malfunctioning, say due to the imminent breaking of some part, or a member of a vehicle fleet that has experienced unusual or hazardous environmental conditions. Monitoring data is one way to detect such problems early by identifying irregular behavior.


80/20 Rule of Data Science: Hear How Fast, Easy Data Integration Can Break It

@machinelearnbot

At this year's Strata Data Conference in New York City, Syncsort's Paige Roberts sat down with John Myers (@johnlmyers44) of Enterprise Management Associates to discuss what he sees in the evolving Big Data landscape. In this final blog in the three-part interview, we'll discuss the 80/20 rule of data science which points out that most data scientists spend 80% of their time getting data ready for analysis, rather than doing what they do best.


Talend Recognized in CRN's Big Data 100 List for Third Consecutive Year - NASDAQ.com

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REDWOOD CITY, Calif., May 22, 2018 (GLOBE NEWSWIRE) -- Talend (NASDAQ: TLND), a leader in cloud data integration solutions, has been named to the 2018 CRN Big Data 100 list, a brand of The Channel Company. This annual list recognizes vendors that have demonstrated an ability to innovate in bringing to market products and services that help businesses work with one of the most dynamic, fastest growing segments of the IT industry - Big Data. As a result of Talend's inclusion in the CRN Big Data 100, Solutions Review selected Talend Open Studio for Data Integration and Talend Cloud among its list of "7 Data Integration Tools We Recommend". The data explosion in recent years has fueled a vibrant big data technology industry. Businesses need innovative products and services to capture, integrate, manage and analyze the massive volumes of data they are grappling with every day.