Book Image

R Data Analysis Solutions - Machine Learning Techniques [Video]

By : Shanthi Viswanathan, Viswa Viswanathan
Book Image

R Data Analysis Solutions - Machine Learning Techniques [Video]

By: Shanthi Viswanathan, Viswa Viswanathan

Overview of this book

<p>Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it. This video empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. You will learn to carry out different tasks on the data to bring it into action.By the end of this course, you will be able to carry out different analyzing techniques, apply classification and regression, and also reduce data.</p> <h1>Style and Approach</h1> <p>This course follows a recipe-based approach. Here each video presents you with a step-by-step approach to performing many important data analytics tasks.</p>

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R Data Analysis Solutions - Machine Learning Techniques [Video]
Shanthi Viswanathan | Viswa Viswanathan
Jul, 2017 | 3 hours 12 minutes
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Table of Contents (5 chapters)
Free Chapter
1
Acquire and Prepare the Ingredients – Your Data
The Course Overview
Reading Data from CSV Files
Reading XML and JSON Data
Reading Data from Fixed-Width Formatted Files, R Files, and R Libraries
Removing and Replacing Missing Values
Removing Duplicate Cases
Rescaling a Variable
Normalizing or Standardizing Data in a Data Frame
Binning Numerical Data
Creating Dummies for Categorical Variables
2
What's in There? – Exploratory Data Analysis
Creating Standard Data Summaries
Extracting Subset of a Dataset
Splitting a Dataset
Creating Random Data Partitions
Generating Standard Plots
Generating Multiple Plots
Selecting a Graphics Device
Creating Plots with the Lattice and ggplot2package
Creating Charts that Facilitate Comparisons
Creating Charts that Visualize Possible Causality
Creating Multivariate Plots
3
Where Does It Belong? – Classification
Generating Error/Classification-Confusion Matrices
Generating ROC Charts
Building, Plotting, and Evaluating – Classification Trees
Using random Forest Models for Classification
Classifying Using the Support Vector Machine Approach
Classifying Using the Naïve Bayes Approach
Classifying Using the KNN Approach
Using Neural Networks for Classification
Classifying Using Linear Discriminant Function Analysis
Classifying Using Logistic Regression
Using AdaBoost to Combine Classification Tree Models
4
Give Me a Number – Regression
Computing the Root Mean Squared Error
Building KNN Models for Regression
Performing Linear Regression
Performing Variable Selection in Linear Regression
Building Regression Trees
Building Random Forest Models for Regression
Using Neural Networks for Regression
Performing k-Fold Cross-Validation and Leave-One-Out-Cross-Validation
5
Can You Simplify That? – Data Reduction Techniques
Performing Cluster Analysis Using K-Means Clustering
Performing Cluster Analysis Using Hierarchical Clustering
Reducing Dimensionality with Principal Component Analysis
Chapter 5
Can You Simplify That? – Data Reduction Techniques
Content Locked
Section 2
Performing Cluster Analysis Using Hierarchical Clustering
The hclust function in the package stats helps us perform hierarchical clustering. - Define a convenience function to standardize the relevant variables - Use the convenience function to standardize the variables of interest - Compute the distance matrix to provide as input to the hclust function
Previous Section
End of Section 2
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