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Mining Weekly - November 03, 2017 English | 40 pages | True PDF | 11.9 MB INSIGHT Relook at low-carat pipes could spring big diamond surprises. NEWS FOCUS Despite regulatory creep, Mali remains 'model' mining country - Randgold. FEATURES Mine Mechanisation & Equipment and Project Management.. https://fikper.com/Zx7s5rF7SD/ https://fileaxa.com/we1xu0va1w7l https://ddownload.com/q5sdru8shege https://rapidgator.net/file/05fc657e7e61cc95d2a683e874a8e879/ https://turbobit.net/u3t5w8yci3yv.html Rapidgator links are free direct download only for my subscriber, other hosts are free download for free users

Mining Weekly - September 22, 2017 English | 40 pages | True PDF | 12.3 MB Global mining news weekly SECONDARY PRODUCTION Two of South Africa's platinum miners enter recycling market South African mining must be made globally competitive, says Motsepe Tailings-savvy DRDGold inviting collaboration, consolidation https://rapidgator.net/file/23bc7772e079dbc5345cb6a4145761fe/ [img=https://ddownload.com/images/promo/banner_240-32.png] https://ddownload.com/7s4x1ys36049 https://nitroflare.com/view/D1AF21837603C40/

Mining the Social Web - Twitter MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 40M | 360 MB Genre: eLearning | Language: English Interested in tapping into Twitter data so you can discover what's trending, what people are talking about, and what feelings are being expressed in people's tweets? This course teaches you how to use a powerful set of tools that will allow you to acquire, analyze, and summarize Twitter data. You'll learn the meanings within Twitter's metadata, explore the data mining techniques of frequency analysis and sentiment, and gain experience using Python as a data mining tool. Learners should be familiar with Jupyter Notebooks and be able to install Python packages on their own using the command line. Learn how to interpret the metadata that accompanies every Tweet Master the ability to connect to the Twitter API using Python Acquire real life experience using Python for data mining Understand how to perform a frequency analysis of different words, users, or hashtags Learn to measure the emotional tone of Tweets by performing a sentiment analysis Gain experience downloading live Twitter datastreams and analyzing them for trends Download link: http://rapidgator.net/file/365d2b0b8162c9aa110461a3efa7444a/hunen.Mining.the.Social.Web..Twitter.rar.html http://nitroflare.com/view/C98B87ACACE2176/hunen.Mining.the.Social.Web..Twitter.rar https://uploadgig.com/file/download/F5d43E5B50289bc8/hunen.Mining.the.Social.Web..Twitter.rar http://uploaded.net/file/tzcjo3wy/hunen.Mining.the.Social.Web..Twitter.rar Links are Interchangeable - No Password - Single Extraction

Mining the Social Web - Twitter Video Training MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 40M | 360 MB Genre: eLearning | Language: English Interested in tapping into Twitter data so you can discover what's trending, what people are talking about, and what feelings are being expressed in people's tweets? This course teaches you how to use a powerful set of tools that will allow you to acquire, analyze, and summarize Twitter data. You'll learn the meanings within Twitter's metadata, explore the data mining techniques of frequency analysis and sentiment, and gain experience using Python as a data mining tool. Learners should be familiar with Jupyter Notebooks and be able to install Python packages on their own using the command line. Learn how to interpret the metadata that accompanies every Tweet Master the ability to connect to the Twitter API using Python Acquire real life experience using Python for data mining Understand how to perform a frequency analysis of different words, users, or hashtags Learn to measure the emotional tone of Tweets by performing a sentiment analysis Gain experience downloading live Twitter datastreams and analyzing them for trends Download From NitroFlare http://nitroflare.com/view/CFFD2BD149F79AF/xidau123_Mining_SocialWeb_Twitter.rar Download From Rapidgator http://rapidgator.net/file/64486a8b82583d1857c29ab0be4e5fbf/xidau123_Mining_SocialWeb_Twitter.rar.html Download From UploadGig https://uploadgig.com/file/download/Cf69e6562708cd74/xidau123_Mining_SocialWeb_Twitter.rar

Text Mining & Natural Language Understanding at Scale Training Video 2016-07-27 | SKU: 02392 | .MP4, AVC, 1000 kbps, 1280x720 | English, AAC, 128 kbps, 2 Ch | 2.25 hours | 871 MB Instructors: David Talby, Claudiu Branzan A text mining system must go way beyond indexing and search to appear truly intelligent. First, it should understand language beyond keyword matching. For example, it should be able to distinguish the critical difference between "Jane has the flu" and "Jane had the flu when she was 9". Second, it should be capable of making likely inferences even if they're not explicitly written. For example, inferring that Jane may have the flu if she has had a fever, headache, fatigue, and runny nose for three days. And third, it should do its work as part of a robust, scalable, efficient and easy to extend system. This course teaches software engineers and data scientists how to build intelligent natural language understanding (NLU) based text mining systems at scale using Java, Scala and Spark for distributed processing. * Learn the meaning of natural language understanding (NLU) and its use in text mining * Discover how to build a natural language processing (NLP) pipeline within a big data framework * Recognize the differences between NLP pipelines and other approaches to semantic text mining * Learn about standard UIMA annotators, custom annotators, and machine learned annotators * Discover how different types of annotators are composed into a text processing pipeline * Use machine learning to generate annotators and apply them within a data pipeline * See pipeline architectures that incorporate Kafka, Spark, SparkSQL, Cassandra, and ElasticSearch DOWNLOAD http://rapidgator.net/file/a609b80a1a1c8173a804737109492083/9qhkb.Text.Mining..Natural.Language.Understanding.at.Scale.Training.Video.rar.html http://uploaded.net/file/ev3cdkcx/9qhkb.Text.Mining..Natural.Language.Understanding.at.Scale.Training.Video.rar https://www.bigfile.to/file/m3tG4MnuF37f/9qhkb.Text.Mining..Natural.Language.Understanding.at.Scale.Training.Video.rar http://nitroflare.com/view/EF4C6948FA838D7/9qhkb.Text.Mining..Natural.Language.Understanding.at.Scale.Training.Video.rar http://uploadgig.com/file/download/c0ae742760861A4f/9qhkb.Text.Mining..Natural.Language.Understanding.at.Scale.Training.Video.rar

Lynda - Data Science Foundations: Data Mining Size: 634 MB | Duration: 4h 41m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch Genre: eLearning | Level: Intermediate | Language: English All data science begins with good data. Data mining is a framework for collecting, searching, and filtering raw data in a systematic matter, ensuring you have clean data from the start. It also helps you parse large data sets, and get at the most meaningful, useful information. This course, Data Science Foundations: Data Mining, is designed to provide a solid point of entry to all the tools, techniques, and tactical thinking behind data mining. Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining. Topics include: * Prerequisites for data mining * Data mining using R, Python, Orange, and RapidMiner * Data reduction * Data clustering * Anomaly detection * Association analysis * Regression analysis * Sequence mining * Text mining Download link: http://rapidgator.net/file/fe862f41b80aaaa6129b39010eec7942/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part1.rar.html]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part1.rar.html http://rapidgator.net/file/2f0c358b36227fb3df991b5bc094e8c5/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part2.rar.html]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part2.rar.html http://rapidgator.net/file/7ee32f38b1536d66364862d0f9dd9dae/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part3.rar.html]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part3.rar.html http://rapidgator.net/file/c272e026e0252256f05ab11835943167/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part4.rar.html]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part4.rar.html http://nitroflare.com/view/B5CDF2775FA2FC8/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part1.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part1.rar http://nitroflare.com/view/FBAF320881AD819/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part2.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part2.rar http://nitroflare.com/view/994ACCB9DD8AD22/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part3.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part3.rar http://nitroflare.com/view/8C0BBEDFD510B50/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part4.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part4.rar http://uploaded.net/file/ostwzigj/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part1.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part1.rar http://uploaded.net/file/nq46uvcd/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part2.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part2.rar http://uploaded.net/file/24jo7jan/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part3.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part3.rar http://uploaded.net/file/xopex7bb/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part4.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part4.rar https://www.bigfile.to/file/dmZ8yUPRtCTb/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part1.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part1.rar https://www.bigfile.to/file/gnmTPTj5Nyuk/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part2.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part2.rar https://www.bigfile.to/file/TwE7BTUkeeGR/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part3.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part3.rar https://www.bigfile.to/file/zTcyzrjzADDW/uyif3.Lynda..Data.Science.Foundations.Data.Mining.part4.rar]uyif3.Lynda..Data.Science.Foundations.Data.Mining.part4.rar Links are Interchangeable - No Password - Single Extraction

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More Data Mining with R MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 10.5 Hours | Lec: 67 | 3.06 GB Genre: eLearning | Language: English How to perform market basket analysis, analyze social networks, mine Twitter data, text, and time series data. More Data Mining with R presents a comprehensive overview of a myriad of contemporary data mining techniques. More Data Mining with R is the logical follow-on course to the preceding Udemy course More Data Mining with R: Go from Beginner to Advanced although it is not necessary to take these courses in sequential order. Both courses examine and explain a number of data mining methods and techniques, using concrete data mining modeling examples, extended case studies, and real data sets. Whereas the preceding More Data Mining with R: Go from Beginner to Advanced course focuses on: (1) linear, logistic and local polynomial regression; (2) decision, classification and regression trees (CART); (3) random forests; and (4) cluster analysis techniques, this course, More Data Mining with R presents detailed instruction and plentiful "hands-on" examples about: (1) association analysis (or market basket analysis) and creating, mining and interpreting association rules using several case examples; (2) network analysis, including the versatile iGraph visualization capabilities, as well as social network data mining analysis cases (marriage and power; friendship links); (3) text mining using Twitter data and word clouds; (4) text and string manipulation, including the use of 'regular expressions'; (5) time series data mining and analysis, including an extended case study forecasting house price indices in Canberra, Australia. DOWNLOAD http://rapidgator.net/file/fab9bedeec5c24b0b47810bbedd1e613/unu6d.More.Data.Mining.with.R.part1.rar.html http://rapidgator.net/file/f1e91d2015543b0425374d09b090cbb1/unu6d.More.Data.Mining.with.R.part2.rar.html http://rapidgator.net/file/234006d2fb070ca57d890218664c0429/unu6d.More.Data.Mining.with.R.part3.rar.html http://rapidgator.net/file/c5ceb195cccc10462bab012b3bcb9a99/unu6d.More.Data.Mining.with.R.part4.rar.html https://bytewhale.com/fsqu7jv4pl78/unu6d.More.Data.Mining.with.R.part1.rar https://bytewhale.com/q31sy29es5an/unu6d.More.Data.Mining.with.R.part2.rar https://bytewhale.com/j3bhii18k0v5/unu6d.More.Data.Mining.with.R.part3.rar https://bytewhale.com/6t0qfbof3cj6/unu6d.More.Data.Mining.with.R.part4.rar http://uploaded.net/file/icwwp3u0/unu6d.More.Data.Mining.with.R.part1.rar http://uploaded.net/file/yne7bqr7/unu6d.More.Data.Mining.with.R.part2.rar http://uploaded.net/file/egc1u9wn/unu6d.More.Data.Mining.with.R.part3.rar http://uploaded.net/file/1vq1am8k/unu6d.More.Data.Mining.with.R.part4.rar https://www.bigfile.to/file/xrpeHDjU7Yw4/unu6d.More.Data.Mining.with.R.part1.rar https://www.bigfile.to/file/agMh83zJa6NP/unu6d.More.Data.Mining.with.R.part2.rar https://www.bigfile.to/file/5Rcs5WZ72EK6/unu6d.More.Data.Mining.with.R.part3.rar https://www.bigfile.to/file/YxBTehQQMqnb/unu6d.More.Data.Mining.with.R.part4.rar http://nitroflare.com/view/96CEC91ABCCAC15/unu6d.More.Data.Mining.with.R.part1.rar http://nitroflare.com/view/E2AA7B9D6F44149/unu6d.More.Data.Mining.with.R.part2.rar http://nitroflare.com/view/FDC089D08A9F9D7/unu6d.More.Data.Mining.with.R.part3.rar http://nitroflare.com/view/F6E39B0FED303D5/unu6d.More.Data.Mining.with.R.part4.rar http://uploadgig.com/file/download/b490d931DAa19d68/unu6d.More.Data.Mining.with.R.part1.rar http://uploadgig.com/file/download/fa3e2293d7735733/unu6d.More.Data.Mining.with.R.part2.rar http://uploadgig.com/file/download/c51566c3C1452E8b/unu6d.More.Data.Mining.with.R.part3.rar http://uploadgig.com/file/download/326549FC1103d2d8/unu6d.More.Data.Mining.with.R.part4.rar

Udemy - Data Mining with Rattle English | MP4 | 1280x720 | 57 kbps | 44 KHz | 15 hours | 2.24 Gb Genre: eLearning Learn to use the GUI-based comprehensive Data Miner data mining software suite implemented as the rattle package in R Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the 'Rattle' package in R software. Rattle is a popular GUI-based software tool which 'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a 'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package. The course is ideal for undergraduate students seeking to master additional 'in-demand' analytical job skills to offer a prospective employer. The course is also suitable for graduate students seeking to learn a variety of techniques useful to analyze research data. Finally, the course is useful for practicing quantitative analysis professionals who seek to acquire and master a wider set of useful job skills and knowledge. The course topics are scheduled in 10 distinct topics, each of which should be the focus of study for a course parti[beeep]nt in a separate week per section topic. What are the requirements? Students will need to install the R console and RStudio software (instructions are provided). What am I going to get from this course? Over 82 lectures and 15 hours of content! Perform and support life-cycle data mining tasks and activities using the popular Data Miner ("Rattle") software suite. Understand the functionalities implicit in the data, explore, test, transform, cluster, associate, model, evaluate, and log tabs in the Data Miner ("Rattle") GUI software platform. Know how to explore, visualize, transform, and summarize data sets in Rattle. Know how to create advanced, interactive Ggobi visualizations of data. Know how to use, estimate and interpret: cluster analyses; association analyses mining rules; decision trees; random forests; boosting; and support vector machines using Rattle. What is the target audience? Anyone interested in data mining seeking to master the use of a powerful popular contemporary (and no-cost) Data Mining software suite Data analytics professionals seeking to augment their data mining skill sets with a popular and useful data mining package. Undergraduate and graduate students seeking to attain in-demand data mining skills for data analysis/mining tasks to offer to prospective employers. DOWNLOAD http://rapidgator.net/file/a977643c800cfd75f1ec325fed71df26/bDataMining.part1.rar.html http://rapidgator.net/file/cca1ef0e1273d1c1617b2fe7c25b8699/bDataMining.part2.rar.html http://rapidgator.net/file/0e7d6e1fac9dc302f94e1cce157e7094/bDataMining.part3.rar.html http://rapidgator.net/file/676d32a2656184120c4ab02e67ae4a63/bDataMining.part4.rar.html http://rapidgator.net/file/d8609daeae87dec013968d7c552fdfc5/bDataMining.part5.rar.html http://uploaded.net/file/ko1cz1ma/bDataMining.part1.rar http://uploaded.net/file/8g7zbfge/bDataMining.part2.rar http://uploaded.net/file/1m3p7pye/bDataMining.part3.rar http://uploaded.net/file/gtvgbepa/bDataMining.part4.rar http://uploaded.net/file/1utvwms2/bDataMining.part5.rar http://www.hitfile.net/6lGv/bDataMining.part1.rar.html http://www.hitfile.net/6kub/bDataMining.part2.rar.html http://www.hitfile.net/6lHx/bDataMining.part3.rar.html http://www.hitfile.net/6kmi/bDataMining.part4.rar.html http://www.hitfile.net/6l73/bDataMining.part5.rar.html http://www.uploadable.ch/file/tPpVYXgcx3kn/bDataMining.part1.rar http://www.uploadable.ch/file/SqxAWqvr5cjU/bDataMining.part2.rar http://www.uploadable.ch/file/v6whzvtx7fHU/bDataMining.part3.rar http://www.uploadable.ch/file/NAcJr3MvAwH8/bDataMining.part4.rar http://www.uploadable.ch/file/W2XheyF3sMGu/bDataMining.part5.rar

Udemy - Case Studies in Data Mining with R English | MP4 | 1280x720 | 61 kbps | 44 KHz | 21 hours | 7.14 Gb Genre: eLearning Learn to use the "Data Mining with R" (DMwR) package and R software to build and evaluate predictive data mining models. Case Studies in Data Mining was originally taught as three separate online data mining courses. We examine three case studies which together present a broad-based tour of the basic and extended tasks of data mining in three different domains: (1) predicting algae blooms; (2) detecting fraudulent sales transactions; and (3) predicting stock market returns. The cumulative "hands-on" 3-course fifteen sessions showcase the use of Luis Torgo's amazingly useful "Data Mining with R" (DMwR) package and R software. Everything that you see on-screen is included with the course: all of the R scripts; all of the data files and R objects used and/or referenced; as well as all of the R packages' documentation. You can be new to R software and/or to data mining and be successful in completing the course. The first case study, Predicting Algae Blooms, provides instruction regarding the many useful, unique data mining functions contained in the R software 'DMwR' package. For the algae blooms prediction case, we specifically look at the tasks of data pre-processing, exploratory data analysis, and predictive model construction. For individuals completely new to R, the first two sessions of the algae blooms case (almost 4 hours of video and materials) provide an accelerated introduction to the use of R and RStudio and to basic techniques for inputting and outputting data and text. Detecting Fraudulent Transactions is the second extended data mining case study that showcases the DMwR (Data Mining with R) package. readmore The case is specific but may be generalized to a common business problem: How does one sift through mountains of data (401,124 records, in this case) and identify suspicious data entries, or "outliers"? The case problem is very unstructured, and walks through a wide variety of approaches and techniques in the attempt to discriminate the "normal", or "ok" transactions, from the abnormal, suspicious, or "fraudulent" transactions. This case presents a large nuMber of alternative modeling approaches, some of which are appropriate for supervised, some for unsupervised, and some for semi-supervised data scenarios. The third extended case, Predicting Stock Market Returns is a data mining case study addressing the domain of automatic stock trading systems. These four sessions address the tasks of building an automated stock trading system based on prediction models that utilize daily stock quote data. The goal is to predict future returns for the S&P 500 market index. The resulting predictions are used together with a trading strategy to make decisions about generating market buy and sell orders. The case examines prediction problems that stem from the time ordering among data observations, that is, from the use of time series data. It also exemplifies the difficulties involved in translating model predictions into decisions and actions in the context of 'real-world' business applications. What are the requirements? Students will need to install no-cost R software and the no-cost RStudio IDE (instructions are provided). What am I going to get from this course? Over 130 lectures and 21 hours of content! Understand how to implement and evaluate a variety of predictive data mining models in three different domains, each described as extended case studies: (1) harmful plant growth; (2) fraudulent transaction detection; and (3) stock market index changes. Perform sophisticated data mining analyses using the "Data Mining with R" (DMwR) package and R software. Have a greatly expanded understanding of the use of R software as a comprehensive data mining tool and platform. Understand how to implement and evaluate supervised, semi-supervised, and unsupervised learning algorithms. What is the target audience? The course is appropriate for anyone seeking to expand their knowledge and analytical skills related to conducting predictive data mining analyses. The course is appropriate for undergraduate students seeking to acquire additional in-demand job skill sets for business analytics. The course is appropriate for graduate students seeking to acquire additional data analysis skills. Knowledge of R software is not required to successfully complete this course. The course is appropriate for practicing business analytics professionals seeking to acquire additional job skill sets. 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Udemy - Data Mining with Rattle MP4 | Video: 1280x720 | 57 kbps | 44 KHz | Duration: 15 Hours | 2.24 GB Genre: eLearning | Language: English Learn to use the GUI-based comprehensive Data Miner data mining software suite implemented as the rattle package in R Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the 'Rattle' package in R software. Rattle is a popular GUI-based software tool which 'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a 'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package. The course is ideal for undergraduate students seeking to master additional 'in-demand' analytical job skills to offer a prospective employer. The course is also suitable for graduate students seeking to learn a variety of techniques useful to analyze research data. Finally, the course is useful for practicing quantitative analysis professionals who seek to acquire and master a wider set of useful job skills and knowledge. The course topics are scheduled in 10 distinct topics, each of which should be the focus of study for a course parti[beeep]nt in a separate week per section topic. What are the requirements? Students will need to install the R console and RStudio software (instructions are provided). What am I going to get from this course? Over 82 lectures and 15 hours of content! Perform and support life-cycle data mining tasks and activities using the popular Data Miner ("Rattle") software suite. Understand the functionalities implicit in the data, explore, test, transform, cluster, associate, model, evaluate, and log tabs in the Data Miner ("Rattle") GUI software platform. Know how to explore, visualize, transform, and summarize data sets in Rattle. Know how to create advanced, interactive Ggobi visualizations of data. Know how to use, estimate and interpret: cluster analyses; association analyses mining rules; decision trees; random forests; boosting; and support vector machines using Rattle. What is the target audience? Anyone interested in data mining seeking to master the use of a powerful popular contemporary (and no-cost) Data Mining software suite Data analytics professionals seeking to augment their data mining skill sets with a popular and useful data mining package. Undergraduate and graduate students seeking to attain in-demand data mining skills for data analysis/mining tasks to offer to prospective employers. 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Mining the Biomedical Literature English | 2012 | 152 pages | ISBN: 0262017695 | PDF | 3 Mb The introduction of high-throughput methods has transformed biology into a data-rich science. Knowledge about biological entities and processes has traditionally been acquired by thousands of scientists through decades of experimentation and analysis. The current abundance of biomedical data is accompanied by the creation and quick dissemination of new information. Much of this information and knowledge, however, is represented only in text form--in the biomedical literature, lab notebooks, Web pages, and other sources. Researchers' need to find relevant information in the vast amounts of text has created a surge of interest in automated text-analysis. In this book, Hagit Shatkay and Mark Craven offer a concise and accessible introduction to key ideas in biomedical text mining. The chapters cover such topics as the relevant sources of biomedical text; text-analysis methods in natural language processing; the tasks of information extraction, information retrieval, and text categorization; and methods for empirically assessing text-mining systems. Finally, the authors describe several applications that recognize entities in text and link them to other entities and data resources, support the curation of structured databases, and make use of text to enable further prediction and discovery. DOWNLOAD http://rapidgator.net/file/64d8b154a364e9eafe31291571019854/Mining_the.rar.html http://uploaded.net/file/zkpjs2h2/Mining_the.rar http://www.uploadable.ch/file/7uNuhPmYsxpr/Mining_the.rar http://www.hitfile.net/4LNG/Mining_the.rar.html