### Chapter:
Getting Started with R

####
The Course Overview

04m 38s

####
Downloading and Installing R

06m 10s

####
Downloading and Installing RStudio

03m 10s

####
Installing and Loading Packages

05m 46s

####
Reading and Writing Data

05m 54s

####
Using R to Manipulate Data

05m 46s

####
Applying Basic Statistics

04m 47s

####
Getting a Dataset for Machine Learning

02m 38s

### Chapter:
Data Exploration with RMS Titanic

####
Reading a Titanic Dataset from a CSV File

08m 36s

####
Converting Types on Character Variables

03m 5s

####
Detecting Missing Values

03m 18s

####
Imputing Missing Values

04m 30s

####
Exploring and Visualizing Data

04m 24s

####
Predicting Passenger Survival with a Decision Tree

03m 59s

####
Validating the Power of Prediction with a Confusion Matrix

02m 8s

####
Assessing performance with the ROC curve

02m 32s

### Chapter:
R and Statistics

####
Understanding Data Sampling in R

03m 30s

####
Operating a Probability Distribution in R

05m 41s

####
Working with Univariate Descriptive Statistics in R

05m 9s

####
Performing Correlations and Multivariate Analysis

03m 1s

####
Operating Linear Regression and Multivariate Analysis

03m 25s

####
Conducting an Exact Binomial Test

03m 48s

####
Performing Student's t-test

03m 13s

####
Performing the Kolmogorov-Smirnov Test

04m 43s

####
Understanding the Wilcoxon Rank Sum and Signed Rank Test

02m 4s

####
Working with Pearson's Chi-Squared Test

05m 9s

####
Conducting a One-Way ANOVA

04m 15s

####
Performing a Two-Way ANOVA

04m 2s

### Chapter:
Understanding Regression Analysis

####
Fitting a Linear Regression Model with lm

04m 53s

####
Summarizing Linear Model Fits

05m 20s

####
Using Linear Regression to Predict Unknown Values

02m 51s

####
Generating a Diagnostic Plot of a Fitted Model

03m 57s

####
Fitting a Polynomial Regression Model with lm

02m 16s

####
Fitting a Robust Linear Regression Model with rlm

02m 15s

####
Studying a case of linear regression on SLID data

06m 39s

####
Applying the Gaussian Model for Generalized Linear Regression

02m 11s

####
Applying the Poisson model for Generalized Linear Regression

01m 33s

####
Applying the Binomial Model for Generalized Linear Regression

02m 2s

####
Fitting a Generalized Additive Model to Data

03m 13s

####
Visualizing a Generalized Additive Model

01m 26s

####
Diagnosing a Generalized Additive Model

03m 38s

### Chapter:
Classification – Tree, Lazy, and Probabilistic

####
Preparing the Training and Testing Datasets

03m 44s

####
Building a Classification Model with Recursive Partitioning Trees

06m 10s

####
Visualizing a Recursive Partitioning Tree

03m 3s

####
Measuring the Prediction Performance of a Recursive Partitioning Tree

02m 48s

####
Pruning a Recursive Partitioning Tree

02m 37s

####
Building a Classification Model with a Conditional Inference Tree

01m 56s

####
Visualizing a Conditional Inference Tree

02m 38s

####
Measuring the Prediction Performance of a Conditional Inference Tree

02m 10s

####
Classifying Data with the K-Nearest Neighbor Classifier

05m 31s

####
Classifying Data with Logistic Regression

04m 37s

####
Classifying data with the Naïve Bayes Classifier

06m 16s

### Chapter:
Neural Network and SVM

####
Classifying Data with a Support Vector Machine

05m 58s

####
Choosing the Cost of an SVM

02m 56s

####
Visualizing an SVM Fit

03m 33s

####
Predicting Labels Based on a Model Trained by an SVM

03m 48s

####
Training a Neural Network with neuralnet

04m 7s

####
Visualizing a Neural Network Trained by neuralnet

02m 21s

####
Predicting Labels based on a Model Trained by neuralnet

03m 7s

####
Training a Neural Network with nnet

02m 45s

####
Predicting labels based on a model trained by nnet

02m 49s

### Chapter:
Model Evaluation

####
Estimating Model Performance with k-fold Cross Validation

03m 42s

####
Performing Cross Validation with the e1071 Package

03m 22s

####
Performing Cross Validation with the caret Package

02m 59s

####
Ranking the Variable Importance with the caret Package

02m 21s

####
Ranking the Variable Importance with the rminer Package

02m 30s

####
Finding Highly Correlated Features with the caret Package

02m 13s

####
Selecting Features Using the Caret Package

04m 58s

####
Measuring the Performance of the Regression Model

03m 57s

####
Measuring Prediction Performance with a Confusion Matrix

02m 7s

####
Measuring Prediction Performance Using ROCR

02m 46s

####
Comparing an ROC Curve Using the Caret Package

03m 43s

####
Measuring Performance Differences between Models with the caret Package

03m 41s

### Chapter:
Ensemble Learning

####
Classifying Data with the Bagging Method

07m 53s

####
Performing Cross Validation with the Bagging Method

01m 56s

####
Classifying Data with the Boosting Method

06m 4s

####
Performing Cross Validation with the Boosting Method

02m 6s

####
Classifying Data with Gradient Boosting

07m 9s

####
Calculating the Margins of a Classifier

05m 30s

####
Calculating the Error Evolution of the Ensemble Method

02m 18s

####
Classifying Data with Random Forest

07m 1s

####
Estimating the Prediction Errors of Different Classifiers

04m 35s

####
Clustering Data with Hierarchical Clustering

08m 40s

####
Cutting Trees into Clusters

03m 30s

####
Clustering Data with the k-Means Method

04m 10s

####
Drawing a Bivariate Cluster Plot

03m 32s

####
Comparing Clustering Methods

04m 15s

####
Extracting Silhouette Information from Clustering

02m 40s

####
Obtaining the Optimum Number of Clusters for k-Means

02m 48s

####
Clustering Data with the Density-Based Method

06m 42s

####
Clustering Data with the Model-Based Method

04m 38s

####
Visualizing a Dissimilarity Matrix

03m 23s

####
Validating Clusters Externally

04m 12s

### Chapter:
Association Analysis and Sequence Mining

####
Transforming Data into Transactions

03m 35s

####
Displaying Transactions and Associations

02m 14s

####
Mining Associations with the Apriori Rule

07m 24s

####
Pruning Redundant Rules

02m 26s

####
Visualizing Association Rules

05m 6s

####
Mining Frequent Itemsets with Eclat

03m 36s

####
Creating Transactions with Temporal Information

02m 41s

####
Mining Frequent Sequential Patterns with cSPADE

04m 15s

### Chapter:
Dimension Reduction

####
Performing Feature Selection with FSelector

07m 37s

####
Performing Dimension Reduction with PCA

07m 18s

####
Determining the Number of Principal Components Using the Scree Test

03m 34s

####
Determining the Number of Principal Components Using the Kaiser Method

02m 5s

####
Visualizing Nultivariate Data Using biplot

03m 16s

####
Performing Dimension Reduction with MDS

05m 37s

####
Reducing Dimensions with SVD

03m 19s

####
Compressing Images with SVD

03m 5s

####
Performing Nonlinear Dimension Reduction with ISOMAP

04m 34s

####
Performing Nonlinear Dimension Reduction with Local Linear Embedding

04m 54s

### Chapter:
Big Data Analysis with R and Hadoop

####
Preparing the RHadoop Environment

05m 35s

####
Operating HDFS with rhdfs

05m 47s

####
Implementing a Word Count Problem with RHadoop

05m 26s

####
Comparing the Performance between an R MapReduce Program and a Standard R Program

05m 3s

####
Testing and Debugging the rmr2 Program

03m 48s

####
Installing plyrmr

03m 12s

####
Manipulating Data with plyrmr

03m 52s

####
Conducting Machine Learning with RHadoop

04m 38s

####
Configuring RHadoop Clusters on Amazon EMR

05m 28s