Learning Data Mining with R [Video]

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Learning Data Mining with R [Video]

Romeo Kienzler

1 customer reviews
A complete course to help you learn all the relevant aspects of data mining using R

Quick links: > What will you learn?> Table of content

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$72.25
RRP $84.99
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Video Details

ISBN 139781785887499
Course Length2 hours 17 minute

Video Description

Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It can be used for day-to-day data analysis tasks.

Data mining is a very broad topic and takes some time to learn. This course will help you to understand the mathematical basics quickly, and then you can directly apply 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. In every discipline, there exist a variety of different algorithms. At least one algorithm of the various classes of algorithms will be covered to give you a foundation to further apply your knowledge to dive deeper into the different flavors of algorithms.

After completing this course, you will be able to solve real-world data mining problems.

Style and Approach

Through the course, you will come to understand the different disciplines of data mining using hands-on examples where you actually solve real-world problems in R. For every category of algorithm, an example is explained in detail including test data and R code.

Table of Contents

Getting Started – A Motivating Example
The Course Overview
Getting Started with R
Data Preparation and Data Cleansing
The Basic Concepts of R
Data Frames and Data Manipulation
Clustering – A Dating App for Your Data Points
Data Points and Distances in a Multidimensional Vector Space
An Algorithmic Approach to Find Hidden Patterns in Data
A Real-world Life Science Example
R Deep Dive, Why Is R Really Cool?
Example – Using a Single Line of Code in R
R Data Types
R Functions and Indexing
S3 Versus S4 – Object-oriented Programming in R
Association Rule Mining
Market Basket Analysis
Introduction to Graphs
Different Association Types
The Apriori Algorithm
The Eclat Algorithm
The FP-Growth Algorithm
Classification
Mathematical Foundations
The Naive Bayes Classifier
Spam Classification with Naïve Bayes
Support Vector Machines
K-nearest Neighbors
Clustering
Hierarchical Clustering
Distribution-based Clustering
Density-based Clustering
Using DBSCAN to Cluster Flowers Based on Spatial Properties
Cognitive Computing and Artificial Intelligence in Data Mining
Introduction to Neural Networks and Deep Learning
Using the H2O Deep Learning Framework
Real-time Cloud Based IoT Sensor Data Analysis

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
  • Get to know how object-oriented programming is done in R
  • 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

Authors

Table of Contents

Getting Started – A Motivating Example
The Course Overview
Getting Started with R
Data Preparation and Data Cleansing
The Basic Concepts of R
Data Frames and Data Manipulation
Clustering – A Dating App for Your Data Points
Data Points and Distances in a Multidimensional Vector Space
An Algorithmic Approach to Find Hidden Patterns in Data
A Real-world Life Science Example
R Deep Dive, Why Is R Really Cool?
Example – Using a Single Line of Code in R
R Data Types
R Functions and Indexing
S3 Versus S4 – Object-oriented Programming in R
Association Rule Mining
Market Basket Analysis
Introduction to Graphs
Different Association Types
The Apriori Algorithm
The Eclat Algorithm
The FP-Growth Algorithm
Classification
Mathematical Foundations
The Naive Bayes Classifier
Spam Classification with Naïve Bayes
Support Vector Machines
K-nearest Neighbors
Clustering
Hierarchical Clustering
Distribution-based Clustering
Density-based Clustering
Using DBSCAN to Cluster Flowers Based on Spatial Properties
Cognitive Computing and Artificial Intelligence in Data Mining
Introduction to Neural Networks and Deep Learning
Using the H2O Deep Learning Framework
Real-time Cloud Based IoT Sensor Data Analysis

Video Details

ISBN 139781785887499
Course Length2 hours 17 minute
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