R: Mining spatial, text, web, and social media data (notice n° 65522)
[ vue normale ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 04305cam a2200301zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88842877 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250107215910.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250107s2017 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781788293747 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88842877 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Makhabel, Bater |
| 245 01 - TITLE STATEMENT | |
| Title | R: Mining spatial, text, web, and social media data |
| Statement of responsibility, etc. | ['Makhabel, Bater', 'Mishra, Pradeepta', 'Danneman, Nathan'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Packt Publishing |
| Date of production, publication, distribution, manufacture, or copyright notice | 2017 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Create data mining algorithmsAbout This BookDevelop a strong strategy to solve predictive modeling problems using the most popular data mining algorithmsReal-world case studies will take you from novice to intermediate to apply data mining techniquesDeploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is ForThis Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path.What You Will LearnDiscover how to manipulate data in RGet to know top classification algorithms written in RExplore solutions written in R based on R Hadoop projectsApply data management skills in handling large data setsAcquire knowledge about neural network concepts and their applications in data miningCreate predictive models for classification, prediction, and recommendationUse various libraries on R CRAN for data miningDiscover more about data potential, the pitfalls, and inferencial gotchasGain an insight into the concepts of supervised and unsupervised learningDelve into exploratory data analysisUnderstand the minute details of sentiment analysisIn DetailData mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects.Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects.After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:Learning Data Mining with R by Bater MakhabelR Data Mining Blueprints by Pradeepta MishraSocial Media Mining with R by Nathan Danneman and Richard HeimannStyle and approachA complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Makhabel, Bater |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mishra, Pradeepta |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Danneman, Nathan |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88842877">https://international.scholarvox.com/netsen/book/88842877</a> |
| Electronic format type | text/html |
| Host name | |
Pas d'exemplaire disponible.
Réseaux sociaux