Udemy Case Studies in Data Mining with | D4460CC83BB95B053D06D53A6E360247E38875DC

Udemy Case Studies in Data Mining with

Name	Udemy - Case Studies in Data Mining with R	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	7.7 GB
Total Files	130
Hash	D4460CC83BB95B053D06D53A6E360247E38875DC

/12 Prediction Tasks and Models/
009 The Prediction Tasks.mp4	48.2 MB
001 Prelude to Modeling Stock Market Indices.mp4	19.6 MB
006 Random Forests Review.mp4	47.2 MB
007 Create Initial Model part 1.mp4	67.1 MB
005 Decision Trees part 4.mp4	48.8 MB
002 Decision Trees as Applicable to Case Study Tasks.mp4	49.1 MB
010 Precision and Recall and Confusion Matrices.mp4	50.2 MB
011 Neural Network Prediction Technique part 1.mp4	75.6 MB
003 Decision Trees part 2.mp4	63.6 MB
004 Decision Trees part 3.mp4	67.6 MB
008 Create Initial Model part 2.mp4	79.2 MB
012 Neural Network Prediction Technique part 2.mp4	68.0 MB
/13 Prediction Models and Support Vector Machines SVMs/
004 SVMs Applied to Stock Market Case.mp4	54.0 MB
006 Multivariate Adaptive Regressive Splines.mp4	53.3 MB
008 Two Strategies.mp4	49.7 MB
003 Review Support Vector Machines SVMs using Weather Data part 3.mp4	38.0 MB
007 How Will the Predictions be Used .mp4	52.1 MB
002 Review Support Vector Machines SVMs using Weather Data part 2.mp4	49.9 MB
009 Writing a Simulated Trader Function part 1.mp4	53.0 MB
005 Kernel Functions.mp4	42.5 MB
001 Review Support Vector Machines SVMs using Weather Data part 1.mp4	45.4 MB
011 Evaluating our Simulated Trades.mp4	47.8 MB
010 Writing a Simulated Trader Function part 2.mp4	42.7 MB
/03 Introduction to Predicting Algae Blooms/
001 Predicting Algae Blooms.mp4	74.4 MB
009 Imputation Replace Missing Values through Correlation.mp4	89.9 MB
006 Imputation Dealing with Unknown or Missing Values.mp4	84.0 MB
007 Imputation Removing Rows with Missing Values.mp4	60.2 MB
008 Imputation Replace Missing Values with Central Measures.mp4	68.8 MB
005 Data Visualization Conditioning Plots.mp4	63.5 MB
003 Data Visualization and Summarization Histograms.mp4	66.5 MB
002 Visualizing other Imputations with Lattice Plots.mp4	66.9 MB
004 Data Visualization Boxplot and Identity Plot.mp4	50.4 MB
/07 Pre-Processing the Data to Apply Methodology/
006 Semi-Supervised Techniques.mp4	50.1 MB
005 Defining Data Mining Tasks.mp4	85.6 MB
008 Lift Charts and Precision Recall Curves.mp4	91.3 MB
004 Pre-Processing the Data part 3.mp4	96.2 MB
003 Pre-Processing the Data part 2.mp4	59.0 MB
002 Pre-Processing the Data part 1.mp4	66.1 MB
007 Precision and Recall.mp4	57.2 MB
001 Review the Data and the Focus of the Fraudulent Transactions Case.mp4	82.9 MB
/01 A Brief Introduction to R and RStudio using Scripts/
001 Course Overview.mp4	8.2 MB
013 Data Structures Dataframes part 2.mp4	59.8 MB
014 Creating New Functions.mp4	73.1 MB
005 Factors part 1.mp4	42.9 MB
011 Data Structures Lists.mp4	64.8 MB
009 Data Structures Matrices and Arrays part 1.mp4	44.9 MB
010 Data Structures Matrices and Arrays part 2.mp4	41.4 MB
007 Generating Sequences.mp4	88.6 MB
004 Data Structures Vectors part 2.mp4	50.1 MB
002 Introduction to R for Data Mining.mp4	92.2 MB
012 Data Structures Dataframes part 1.mp4	51.7 MB
006 Factors part 2.mp4	54.4 MB
008 Indexing aka Subscripting or Subsetting.mp4	43.2 MB
003 Data Structures Vectors part 1.mp4	45.9 MB
/06 Examine the Data in the Fraudulent Transactions Case Study/
002 Fraudulent Case Study Introduction.mp4	11.7 MB
005 Continue Exploring the Data.mp4	51.7 MB
001 Exercise Solution from Evaluating and Selecting Models.mp4	20.5 MB
004 Exploring the Data with Eye toward Missingness.mp4	66.9 MB
003 Prelude to Exploring the Data.mp4	20.4 MB
/05 Evaluating and Selecting Models/
004 Setting up K-Fold Evaluation part 2.mp4	57.5 MB
003 Setting up K-Fold Evaluation part 1.mp4	75.7 MB
008 Predicting from the Models.mp4	78.7 MB
009 Comparing the Predictions.mp4	70.2 MB
007 Finish Evaluating Models.mp4	68.9 MB
001 Alternative Model Evaluation Criteria.mp4	79.8 MB
006 Best Model part 2.mp4	58.3 MB
002 Introduction to K-Fold Cross-Validation.mp4	69.2 MB
005 Best Model part 1.mp4	46.6 MB
/08 Methodology to Find Outliers Fraudulent Transactions/
004 Cumulative Recall Chart.mp4	54.9 MB
009 Experimental Methodology to find Outliers part 4.mp4	67.0 MB
001 Exercise from Previous Session.mp4	13.4 MB
003 Review Lift Charts and Precision Recall Curves.mp4	51.7 MB
007 Experimental Methodology to find Outliers part 2.mp4	74.1 MB
005 Creating More Functions for the Experimental Methodology.mp4	39.8 MB
002 Review Precision and Recall.mp4	50.5 MB
006 Experimental Methodology to find Outliers part 1.mp4	60.3 MB
010 Experimental Methodology to find Outliers part 5.mp4	35.0 MB
008 Experimental Methodology to find Outliers part 3.mp4	70.8 MB
/02 Inputting and Outputting Data and Text/
005 Example Program powers.R.mp4	50.7 MB
002 Using the scan Function for Input part 2.mp4	25.1 MB
001 Using the scan Function for Input part 1.mp4	26.3 MB
003 Using readline, cat and print Functions.mp4	46.1 MB
008 Reading and Writing Files part 2.mp4	62.1 MB
004 Using readLines Function and Text Data.mp4	61.3 MB
006 Example Program quad2b.R.mp4	50.7 MB
007 Reading and Writing Files part 1.mp4	23.7 MB
/10 Sidebar on Boosting/
004 Replicating Adaboost using Rpart part 2.mp4	87.6 MB
006 Boosting Exercise.mp4	47.0 MB
002 Boosting Demo Basics using R.mp4	54.4 MB
003 Replicating Adaboost using Rpart Recursive Partitioning Package.mp4	76.6 MB
001 Introduction to Boosting from Rattle course.mp4	56.9 MB
005 Boosting Extensions and Variants.mp4	89.0 MB
/15 Wrap Up Stock Market Case Study/
003 Last Session Wrap-Up part 2.mp4	52.5 MB
001 Prologue to Last Session Wrap-Up.mp4	72.8 MB
002 Last Session Wrap-Up part 1.mp4	63.2 MB
/11 Introduction to Stock Market Prediction Case Study/
004 Accessing the Data part 1.mp4	55.9 MB
010 Defining the Prediction Tasks part 5.mp4	44.8 MB
003 Case Study Background and Data part 2.mp4	71.7 MB
002 Case Study Background and Data part 1.mp4	73.3 MB
001 Introduction to Stock Market Case Study and Materials.mp4	15.7 MB
005 Accessing the Data part 2.mp4	45.3 MB
007 Defining the Prediction Tasks part 2.mp4	79.0 MB
009 Defining the Prediction Tasks part 4.mp4	45.9 MB
006 Defining the Prediction Tasks part 1.mp4	66.2 MB
008 Defining the Prediction Tasks part 3.mp4	62.7 MB
/04 Obtaining Prediction Models/
003 Examine Alternative Regression Models.mp4	110.1 MB
005 Strategy for Pruning Trees.mp4	68.0 MB
002 Creating Prediction Models.mp4	112.0 MB
001 Read in Data Files.mp4	81.8 MB
004 Regression Trees.mp4	100.6 MB
/09 The Data Mining Tasks to Find the Fraudulent Transactions/
003 Review of Fraud Case part 3.mp4	57.9 MB
001 Review of Fraud Case part 1.mp4	61.6 MB
005 Local Outlier Factors.mp4	70.9 MB
004 Baseline Boxplot Rule.mp4	40.4 MB
009 SMOTE and Naive Bayes part 2.mp4	54.1 MB
007 Supervised and Unsupervised Approaches.mp4	77.7 MB
002 Review of Fraud Case part 2.mp4	59.5 MB
008 SMOTE and Naive Bayes part 1.mp4	64.4 MB
006 Plotting Everything.mp4	52.2 MB
/14 Model Evaluation and Selection/
001 Quick Review of Case Study Support Vector Machines SVMs.mp4	58.7 MB
010 Continue Evaluating part 2.mp4	65.8 MB
005 So What Approach is Recommended .mp4	49.8 MB
004 Why You Cannot Randomly Resample Records.mp4	46.9 MB
011 Continue Evaluating part 3.mp4	57.0 MB
003 Evaluating Policy One and Policy Two.mp4	51.3 MB
006 Experimental Model Comparisons part 1.mp4	59.9 MB
008 Set Up Ranksystems.mp4	82.1 MB
002 Begin Evaluating Models.mp4	75.3 MB
009 Continue Evaluating part 1.mp4	58.5 MB
007 Experimental Model Comparisons part 2.mp4	65.8 MB
Total files 130