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Learning Data Mining with R
Learning Data Mining with R

Learning Data Mining with R:

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Profile Icon Romeo Kienzler
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Course Aug 2016 1783 pages 1st Edition
Video
$9.99 $39.99
Arrow left icon
Profile Icon Romeo Kienzler
Arrow right icon
Course Aug 2016 1783 pages 1st Edition
Video
$9.99 $39.99
Video
$9.99 $39.99

Key benefits

  • Use powerful R libraries to effectively get the most out of your data
  • Gain a good level of knowledge and an understanding of the data mining disciplines to solve real-world challenges in R
  • Learn to tackle complex challenges pertaining to different data patterns

Description

Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is one of the most popular programming languages for statistical analysis. This course will get you up and running with the mathematical basics quickly, which you can directly apply based on what you’ve learned in R. This course covers each and every aspect of data mining in order to prepare you for real-world problems. You'll come to understand the different disciplines in data mining and then delve into a variety of algorithms based on each discipline. At least one of the various classes of algorithms will be covered to broaden your knowledge to dive deeper into the different flavors of these algorithms. After completing this course, you will be able to solve real-world data mining problems.

Who is this book for?

This video appeals to anyone who works with a lot of data and wants to make sense of this data in the most efficient manner. The video will appeal to aspiring data scientists, data analysts, and anyone who wishes to perform advanced analytics in future.Basic familiarity with R programming is expected.

What you will learn

  • * Get to know the basic concepts of R: the data frame and data manipulation
  • * Discover the powerful tools at hand for data preparation and data cleansing
  • * Visually find patterns in data
  • * Work with complex data sets and understand how to process data sets
  • * Explore graphs and the statistical measure in graphs
  • * Gain insights into the different association types
  • * Decide what algorithms actually should be used and what the desired and possible outcomes of the analysis should be
  • * Grasp the discipline of classification, the mathematical foundation that will help you understand the bayes theorem and the naïve bayes classifier
  • * Delve into various algorithms for classification such as KNN and see how they are applied in R
  • * Evaluate k-Means, Connectivity, Distribution, and Density-based clustering

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 31, 2016
Length: 1783 pages
Edition : 1st
Language : English
ISBN-13 : 9781786463500
Vendor :
Apache
Category :
Languages :
Concepts :
Tools :

Product Details

Publication date : Aug 31, 2016
Length: 1783 pages
Edition : 1st
Language : English
ISBN-13 : 9781786463500
Vendor :
Apache
Category :
Languages :
Concepts :
Tools :

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Table of Contents

10 Chapters
Table of Contents Chevron down icon Chevron up icon
R: Data Analysis and Visualization Chevron down icon Chevron up icon
I. Module 1: Data Analysis with R Chevron down icon Chevron up icon
II. Module 2: R Graphs Chevron down icon Chevron up icon
III. Module 3: Learning Data Mining with R Chevron down icon Chevron up icon
IV. Module 4: Mastering R for Quantitative Finance Chevron down icon Chevron up icon
V. Module 5: Machine Learning with R module Chevron down icon Chevron up icon
A. Reflect and Test Yourself Answers Chevron down icon Chevron up icon
B. Bibliography Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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