Books & Videos

Table of Contents

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

Visualizing Data

03m 33s

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

Tuning an SVM

02m 47s

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

Chapter: Clustering

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

Installing rmr2

03m 52s

Installing rhdfs

04m 15s

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