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Udemy - Case Studies in Data Mining with R

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Title: UdemyGroup: NOGRPSource: Udemy
Info Hash
D4460CC83BB95B053D06D53A6E360247E38875DC
Source
Unverified
Total Size
7.14 GB
Total Files
100
Seeders
2
Leechers
0
Health
2.00
Score
4
Type
Bookware

File List

FileSize
12 Prediction Tasks and Models/009 The Prediction Tasks.mp445.93 MB
12 Prediction Tasks and Models/001 Prelude to Modeling Stock Market Indices.mp418.73 MB
12 Prediction Tasks and Models/006 Random Forests Review.mp444.97 MB
12 Prediction Tasks and Models/007 Create Initial Model part 1.mp464.02 MB
12 Prediction Tasks and Models/005 Decision Trees part 4.mp446.5 MB
12 Prediction Tasks and Models/002 Decision Trees as Applicable to Case Study Tasks.mp446.82 MB
12 Prediction Tasks and Models/010 Precision and Recall and Confusion Matrices.mp447.85 MB
12 Prediction Tasks and Models/011 Neural Network Prediction Technique part 1.mp472.14 MB
12 Prediction Tasks and Models/003 Decision Trees part 2.mp460.62 MB
12 Prediction Tasks and Models/004 Decision Trees part 3.mp464.46 MB
12 Prediction Tasks and Models/008 Create Initial Model part 2.mp475.5 MB
12 Prediction Tasks and Models/012 Neural Network Prediction Technique part 2.mp464.81 MB
13 Prediction Models and Support Vector Machines SVMs/004 SVMs Applied to Stock Market Case.mp451.47 MB
13 Prediction Models and Support Vector Machines SVMs/006 Multivariate Adaptive Regressive Splines.mp450.8 MB
13 Prediction Models and Support Vector Machines SVMs/008 Two Strategies.mp447.44 MB
13 Prediction Models and Support Vector Machines SVMs/003 Review Support Vector Machines SVMs using Weather Data part 3.mp436.2 MB
13 Prediction Models and Support Vector Machines SVMs/007 How Will the Predictions be Used .mp449.7 MB
13 Prediction Models and Support Vector Machines SVMs/002 Review Support Vector Machines SVMs using Weather Data part 2.mp447.56 MB
13 Prediction Models and Support Vector Machines SVMs/009 Writing a Simulated Trader Function part 1.mp450.53 MB
13 Prediction Models and Support Vector Machines SVMs/005 Kernel Functions.mp440.55 MB
13 Prediction Models and Support Vector Machines SVMs/001 Review Support Vector Machines SVMs using Weather Data part 1.mp443.34 MB
13 Prediction Models and Support Vector Machines SVMs/011 Evaluating our Simulated Trades.mp445.56 MB
13 Prediction Models and Support Vector Machines SVMs/010 Writing a Simulated Trader Function part 2.mp440.69 MB
03 Introduction to Predicting Algae Blooms/001 Predicting Algae Blooms.mp470.92 MB
03 Introduction to Predicting Algae Blooms/009 Imputation Replace Missing Values through Correlation.mp485.69 MB
03 Introduction to Predicting Algae Blooms/006 Imputation Dealing with Unknown or Missing Values.mp480.13 MB
03 Introduction to Predicting Algae Blooms/007 Imputation Removing Rows with Missing Values.mp457.39 MB
03 Introduction to Predicting Algae Blooms/008 Imputation Replace Missing Values with Central Measures.mp465.57 MB
03 Introduction to Predicting Algae Blooms/005 Data Visualization Conditioning Plots.mp460.59 MB
03 Introduction to Predicting Algae Blooms/003 Data Visualization and Summarization Histograms.mp463.39 MB
03 Introduction to Predicting Algae Blooms/002 Visualizing other Imputations with Lattice Plots.mp463.75 MB
03 Introduction to Predicting Algae Blooms/004 Data Visualization Boxplot and Identity Plot.mp448.07 MB
07 Pre-Processing the Data to Apply Methodology/006 Semi-Supervised Techniques.mp447.79 MB
07 Pre-Processing the Data to Apply Methodology/005 Defining Data Mining Tasks.mp481.59 MB
07 Pre-Processing the Data to Apply Methodology/008 Lift Charts and Precision Recall Curves.mp487.05 MB
07 Pre-Processing the Data to Apply Methodology/004 Pre-Processing the Data part 3.mp491.74 MB
07 Pre-Processing the Data to Apply Methodology/003 Pre-Processing the Data part 2.mp456.23 MB
07 Pre-Processing the Data to Apply Methodology/002 Pre-Processing the Data part 1.mp463.08 MB
07 Pre-Processing the Data to Apply Methodology/007 Precision and Recall.mp454.53 MB
07 Pre-Processing the Data to Apply Methodology/001 Review the Data and the Focus of the Fraudulent Transactions Case.mp479.02 MB
01 A Brief Introduction to R and RStudio using Scripts/001 Course Overview.mp47.84 MB
01 A Brief Introduction to R and RStudio using Scripts/013 Data Structures Dataframes part 2.mp457.03 MB
01 A Brief Introduction to R and RStudio using Scripts/014 Creating New Functions.mp469.69 MB
01 A Brief Introduction to R and RStudio using Scripts/005 Factors part 1.mp440.95 MB
01 A Brief Introduction to R and RStudio using Scripts/011 Data Structures Lists.mp461.81 MB
01 A Brief Introduction to R and RStudio using Scripts/009 Data Structures Matrices and Arrays part 1.mp442.79 MB
01 A Brief Introduction to R and RStudio using Scripts/010 Data Structures Matrices and Arrays part 2.mp439.44 MB
01 A Brief Introduction to R and RStudio using Scripts/007 Generating Sequences.mp484.51 MB
01 A Brief Introduction to R and RStudio using Scripts/004 Data Structures Vectors part 2.mp447.78 MB
01 A Brief Introduction to R and RStudio using Scripts/002 Introduction to R for Data Mining.mp487.9 MB
01 A Brief Introduction to R and RStudio using Scripts/012 Data Structures Dataframes part 1.mp449.3 MB
01 A Brief Introduction to R and RStudio using Scripts/006 Factors part 2.mp451.89 MB
01 A Brief Introduction to R and RStudio using Scripts/008 Indexing aka Subscripting or Subsetting.mp441.23 MB
01 A Brief Introduction to R and RStudio using Scripts/003 Data Structures Vectors part 1.mp443.78 MB
06 Examine the Data in the Fraudulent Transactions Case Study/002 Fraudulent Case Study Introduction.mp411.17 MB
06 Examine the Data in the Fraudulent Transactions Case Study/005 Continue Exploring the Data.mp449.26 MB
06 Examine the Data in the Fraudulent Transactions Case Study/001 Exercise Solution from Evaluating and Selecting Models.mp419.53 MB
06 Examine the Data in the Fraudulent Transactions Case Study/004 Exploring the Data with Eye toward Missingness.mp463.78 MB
06 Examine the Data in the Fraudulent Transactions Case Study/003 Prelude to Exploring the Data.mp419.48 MB
05 Evaluating and Selecting Models/004 Setting up K-Fold Evaluation part 2.mp454.83 MB
05 Evaluating and Selecting Models/003 Setting up K-Fold Evaluation part 1.mp472.19 MB
05 Evaluating and Selecting Models/008 Predicting from the Models.mp475.05 MB
05 Evaluating and Selecting Models/009 Comparing the Predictions.mp466.94 MB
05 Evaluating and Selecting Models/007 Finish Evaluating Models.mp465.73 MB
05 Evaluating and Selecting Models/001 Alternative Model Evaluation Criteria.mp476.1 MB
05 Evaluating and Selecting Models/006 Best Model part 2.mp455.58 MB
05 Evaluating and Selecting Models/002 Introduction to K-Fold Cross-Validation.mp466.04 MB
05 Evaluating and Selecting Models/005 Best Model part 1.mp444.43 MB
08 Methodology to Find Outliers Fraudulent Transactions/004 Cumulative Recall Chart.mp452.34 MB
08 Methodology to Find Outliers Fraudulent Transactions/009 Experimental Methodology to find Outliers part 4.mp463.94 MB
08 Methodology to Find Outliers Fraudulent Transactions/001 Exercise from Previous Session.mp412.82 MB
08 Methodology to Find Outliers Fraudulent Transactions/003 Review Lift Charts and Precision Recall Curves.mp449.27 MB
08 Methodology to Find Outliers Fraudulent Transactions/007 Experimental Methodology to find Outliers part 2.mp470.71 MB
08 Methodology to Find Outliers Fraudulent Transactions/005 Creating More Functions for the Experimental Methodology.mp437.96 MB
08 Methodology to Find Outliers Fraudulent Transactions/002 Review Precision and Recall.mp448.12 MB
08 Methodology to Find Outliers Fraudulent Transactions/006 Experimental Methodology to find Outliers part 1.mp457.49 MB
08 Methodology to Find Outliers Fraudulent Transactions/010 Experimental Methodology to find Outliers part 5.mp433.41 MB
08 Methodology to Find Outliers Fraudulent Transactions/008 Experimental Methodology to find Outliers part 3.mp467.48 MB
02 Inputting and Outputting Data and Text/005 Example Program powers.R.mp448.33 MB
02 Inputting and Outputting Data and Text/002 Using the scan Function for Input part 2.mp423.92 MB
02 Inputting and Outputting Data and Text/001 Using the scan Function for Input part 1.mp425.08 MB
02 Inputting and Outputting Data and Text/003 Using readline, cat and print Functions.mp444 MB
02 Inputting and Outputting Data and Text/008 Reading and Writing Files part 2.mp459.2 MB
02 Inputting and Outputting Data and Text/004 Using readLines Function and Text Data.mp458.48 MB
02 Inputting and Outputting Data and Text/006 Example Program quad2b.R.mp448.33 MB
02 Inputting and Outputting Data and Text/007 Reading and Writing Files part 1.mp422.59 MB
10 Sidebar on Boosting/004 Replicating Adaboost using Rpart part 2.mp483.57 MB
10 Sidebar on Boosting/006 Boosting Exercise.mp444.8 MB
10 Sidebar on Boosting/002 Boosting Demo Basics using R.mp451.87 MB
10 Sidebar on Boosting/003 Replicating Adaboost using Rpart Recursive Partitioning Package.mp473.06 MB
10 Sidebar on Boosting/001 Introduction to Boosting from Rattle course.mp454.25 MB
10 Sidebar on Boosting/005 Boosting Extensions and Variants.mp484.89 MB
15 Wrap Up Stock Market Case Study/003 Last Session Wrap-Up part 2.mp450.07 MB
15 Wrap Up Stock Market Case Study/001 Prologue to Last Session Wrap-Up.mp469.43 MB
15 Wrap Up Stock Market Case Study/002 Last Session Wrap-Up part 1.mp460.32 MB
11 Introduction to Stock Market Prediction Case Study/004 Accessing the Data part 1.mp453.34 MB
11 Introduction to Stock Market Prediction Case Study/010 Defining the Prediction Tasks part 5.mp442.69 MB
11 Introduction to Stock Market Prediction Case Study/003 Case Study Background and Data part 2.mp468.39 MB
11 Introduction to Stock Market Prediction Case Study/002 Case Study Background and Data part 1.mp469.88 MB
11 Introduction to Stock Market Prediction Case Study/001 Introduction to Stock Market Case Study and Materials.mp414.95 MB

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