•R Data Analysis Solutions Machine Learning Techniques
•The Course Overview
•Reading Data from CSV Files
•Reading XML and JSON Data
•Reading Data from FixedWidth 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
•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 ggplotpackage
•Creating Charts that Facilitate Comparisons
•Creating Charts that Visualize Possible Causality
•Creating Multivariate Plots
•Generating ErrorClassificationConfusion 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
•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 kFold CrossValidation and LeaveOneOutCrossValidation
•Performing Cluster Analysis Using KMeans Clustering
•Performing Cluster Analysis Using Hierarchical Clustering
•Reducing Dimensionality with Principal Component Analysis
•Machine Learning using Advanced Algorithms and Visualization in R
•Random Forest Overview
•Exploring the Vote Data Set
•Using a Random Forest Model
•Examining the model
•New Model and Final Results
•SVM Overview and EDA
•Building an SVM Model
•Examining the Results and Model
•Visualizing a Confusion Matrix
•Overview of Satellite Data
•Overview of KNearest Neighbor
•Using KNN
•Visualizing KNN Results
•Overview of Movie Review Data
•Overview of Document Vectors
•Classifying Document Matrices
•Clustering Documents
•Similar Documents
•Test your knowledge
•