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Data Analysis Using Machine Learning Techniques
Section 1 Introduction
Lecture 1 Welcome (2:03)
Lecture 2 Reading Data from CSV Files (6:23)
Lecture 3 Reading XML and JSON Data (5:57)
Lecture 4 Reading Data from Fixed-Width Formatted Files, R Files, and R Libraries (6:26)
Lecture 5 Removing and Replacing Missing Values (6:03)
Lecture 6 Removing Duplicate Cases (1:54)
Lecture 7 Rescaling a Variable (2:05)
Lecture 8 Normalizing or Standardizing Data in a Data Frame (2:54)
Lecture 9 Binning Numerical Data (3:17)
Lecture 10 Creating Dummies for Categorical Variables (3:38)
Section 2 Exploratory Data Analysis
Lecture 11 Creating Standard Data Summaries (3:19)
Lecture 12 Extracting Subset of a Dataset (5:36)
Lecture 13 Splitting a Dataset (1:46)
Lecture 14 Creating Random Data Partitions (7:37)
Lecture 15 Generating Standard Plots (5:14)
Lecture 16 Generating Multiple Plots (1:41)
Lecture 17 Selecting a Graphics Device (1:43)
Lecture 18 Creating Plots with the Lattice and ggplot2package (8:54)
Lecture 19 Creating Charts that Facilitate Comparisons (2:30)
Lecture 20 Creating Charts that Visualize Possible Causality (1:23)
Lecture 21 Creating Multivariate Plots (2:02)
Section 3 Classification
Lecture 22 Generating ErrorClassification-Confusion Matrices (4:19)
Lecture 23 Generating ROC Charts (3:38)
Lecture 24 Building, Plotting, and Evaluating – Classification Trees (5:55)
Lecture 25 Using random Forest Models for Classification (4:09)
Lecture 26 Classifying Using the Support Vector Machine Approach (5:16)
Lecture 27 Classifying Using the Naïve Bayes Approach (2:11)
Lecture 28 Classifying Using the KNN Approach (4:51)
Lecture 29 Using Neural Networks for Classification (4:07)
Lecture 30 Classifying Using Linear Discriminant Function Analysis (2:36)
Lecture 31 Classifying Using Logistic Regression (3:48)
Lecture 32 Using AdaBoost to Combine Classification Tree Models (3:19)
Section 4 Recognize the Patterns - Regression
Lecture 33 Computing the Root Mean Squared Error (2:38)
Lecture 34 Building KNN Models for Regression (8:13)
Lecture 35 Performing Linear Regression (7:17)
Lecture 36 Performing Variable Selection in Linear Regression (2:13)
Lecture 37 Building Regression Trees (7:46)
Lecture 38 Building Random Forest Models for Regression (4:58)
Lecture 39 Using Neural Networks for Regression (3:24)
Lecture 40 Performing k-Fold Cross-Validation and Leave-One-Out-Cross-Validation (4:55)
Section 5 Data Reduction Techniques and Summary
Lecture 41 Performing Cluster Analysis Using K-Means Clustering (6:48)
Lecture 42 Performing Cluster Analysis Using Hierarchical Clustering (3:47)
Lecture 43 Reducing Dimensionality with Principal Component Analysis (4:29)
Lecture 25 Using random Forest Models for Classification
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