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00000_Intro.pdf
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PDF
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334.9 KB
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001 Bagging.mp4
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MP4
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32.3 MB
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001 Basics of Decision Trees.mp4
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MP4
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50.6 MB
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001 Boosting techniques.mp4
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MP4
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34.4 MB
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001 Classification Trees.mp4
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MP4
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33 MB
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001 Content flow.mp4
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MP4
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9.8 MB
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001 Gathering Business Knowledge.mp4
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MP4
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25 MB
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001 Installing R and R studio.mp4
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MP4
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40.8 MB
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001 Introduction to Machine Learning.mp4
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MP4
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123.3 MB
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001 Introduction.mp4
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MP4
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21.2 MB
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001 Kernel Based Support Vector Machines.mp4
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MP4
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45.7 MB
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001 Linear Discriminant Analysis.mp4
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MP4
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48.4 MB
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001 Linear models other than OLS.mp4
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MP4
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19 MB
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001 Logistic Regression.mp4
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MP4
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38.8 MB
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001 Random Forest technique.mp4
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MP4
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21.4 MB
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001 Support Vector classifiers.mp4
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MP4
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64.1 MB
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001 Test-Train Split.mp4
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MP4
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45.4 MB
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001 The Data and the Data Dictionary.mp4
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MP4
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87.4 MB
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001 The Data set for the Classification problem.mp4
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MP4
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22 MB
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001 The final milestone!.mp4
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MP4
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11.9 MB
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001 The problem statement.mp4
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MP4
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10.6 MB
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001 Three Classifiers and the problem statement.mp4
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MP4
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22.8 MB
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001 Types of Data.mp4
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MP4
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21.8 MB
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001 Understanding the results of classification models.mp4
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MP4
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45.8 MB
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002 Bagging in R.mp4
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MP4
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69.3 MB
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002 Basic equations and Ordinary Least Squared (OLS) method.mp4
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MP4
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49.9 MB
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002 Building a Machine Learning Model.mp4
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MP4
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44.9 MB
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002 Congratulations & About your certificate.html
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HTML
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2.7 KB
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002 Course Resources.html
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HTML
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1.2 KB
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002 Course resources_ Notes and Datasets.html
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HTML
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921.6 B
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002 Data Exploration.mp4
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MP4
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23.3 MB
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002 Gradient Boosting in R.mp4
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MP4
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78.6 MB
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002 Importing the dataset into R.mp4
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MP4
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16.3 MB
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002 Limitations of Support Vector Classifiers.mp4
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MP4
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13 MB
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002 Linear Discriminant Analysis in R.mp4
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MP4
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89.5 MB
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002 Random Forest in R.mp4
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MP4
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37.4 MB
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002 Subset Selection techniques.mp4
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MP4
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86.7 MB
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002 Summary of the three models.mp4
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MP4
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25.1 MB
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002 Test-Train Split in R.mp4
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MP4
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90.2 MB
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002 The Concept of a Hyperplane.mp4
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MP4
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35.3 MB
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002 The Data set for Classification problem.mp4
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MP4
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21.9 MB
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002 This is a milestone!.mp4
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MP4
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20.7 MB
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002 Training a Simple Logistic model in R.mp4
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MP4
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31 MB
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002 Types of Statistics.mp4
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MP4
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10.9 MB
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002 Understanding a Regression Tree.mp4
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MP4
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52.2 MB
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002 Why can't we use Linear Regression_.mp4
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MP4
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20.2 MB
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003 AdaBoosting in R.mp4
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MP4
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103 MB
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003 Assessing Accuracy of predicted coefficients.mp4
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MP4
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103.9 MB
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003 Basics of R and R studio.mp4
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MP4
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48 MB
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003 Building a classification Tree in R.mp4
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MP4
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100.1 MB
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003 Describing the data graphically.mp4
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MP4
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65.4 MB
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003 EDD in R.mp4
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MP4
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77.8 MB
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003 Importing Data into R.mp4
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MP4
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65.3 MB
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003 K-Nearest Neighbors classifier.mp4
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MP4
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83.3 MB
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003 Maximum Margin Classifier.mp4
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MP4
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26.2 MB
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003 Results of Simple Logistic Regression.mp4
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MP4
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30.9 MB
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003 Subset selection in R.mp4
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MP4
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76.6 MB
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003 The Data and the Data Dictionary.mp4
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MP4
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78.3 MB
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003 The stopping criteria for controlling tree growth.mp4
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MP4
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16.5 MB
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004 Advantages and Disadvantages of Decision Trees.mp4
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MP4
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7.8 MB
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004 Assessing Model Accuracy - RSE and R squared.mp4
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MP4
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49.5 MB
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004 Importing the dataset into R.mp4
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MP4
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15.9 MB
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004 K-Nearest Neighbors in R.mp4
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MP4
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79.6 MB
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004 Limitations of Maximum Margin Classifier.mp4
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MP4
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12.5 MB
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004 Logistic with multiple predictors.mp4
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MP4
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9.9 MB
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004 Measures of Centers.mp4
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MP4
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38.5 MB
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004 Outlier Treatment in R.mp4
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MP4
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31.2 MB
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004 Packages in R.mp4
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MP4
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98.5 MB
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004 Shrinkage methods - Ridge Regression and The Lasso.mp4
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MP4
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38.4 MB
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004 Test-Train Split.mp4
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MP4
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59.4 MB
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004 The Data set for this part.mp4
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MP4
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42 MB
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004 XGBoosting in R.mp4
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MP4
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186.5 MB
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005 Classification SVM model using Linear Kernel.mp4
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MP4
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166.9 MB
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005 Course resources_ Notes and Datasets.html
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HTML
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1 KB
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005 Inputting data part 1_ Inbuilt datasets of R.mp4
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MP4
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46.1 MB
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005 Measures of Dispersion.mp4
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MP4
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22.8 MB
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005 Missing Value imputation in R.mp4
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MP4
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23.4 MB
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005 Ridge regression and Lasso in R.mp4
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MP4
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124 MB
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005 Simple Linear Regression in R.mp4
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MP4
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50.5 MB
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005 Training multiple predictor Logistic model in R.mp4
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MP4
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18.3 MB
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005 Univariate Analysis and EDD.mp4
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MP4
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27.2 MB
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006 Confusion Matrix.mp4
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MP4
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26.6 MB
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006 EDD in R.mp4
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MP4
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112 MB
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006 Hyperparameter Tuning for Linear Kernel.mp4
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MP4
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70.4 MB
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006 Importing the Data set into R.mp4
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MP4
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51.8 MB
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006 Inputting data part 2_ Manual data entry.mp4
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MP4
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30.8 MB
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006 Multiple Linear Regression.mp4
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MP4
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38.7 MB
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006 Variable transformation in R.mp4
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MP4
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46.5 MB
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007 Dummy variable creation in R.mp4
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MP4
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52.5 MB
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007 Evaluating Model performance.mp4
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MP4
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42.5 MB
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007 Inputting data part 3_ Importing from CSV or Text files.mp4
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MP4
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69 MB
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007 Outlier Treatment.mp4
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MP4
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27.7 MB
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007 Polynomial Kernel with Hyperparameter Tuning.mp4
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MP4
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98.7 MB
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007 Splitting Data into Test and Train Set in R.mp4
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MP4
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52.6 MB
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007 The F - statistic.mp4
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MP4
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63.8 MB
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008 Building a Regression Tree in R.mp4
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MP4
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121.9 MB
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008 Creating Barplots in R.mp4
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MP4
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117.2 MB
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008 Interpreting result for categorical Variable.mp4
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MP4
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26.9 MB
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008 Outlier Treatment in R.mp4
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MP4
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37.8 MB
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008 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4
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MP4
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66.1 MB
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008 Radial Kernel with Hyperparameter Tuning.mp4
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MP4
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67.4 MB
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009 Creating Histograms in R.mp4
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MP4
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51.3 MB
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009 Customer.csv
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CSV
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64 KB
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009 Missing Value imputation.mp4
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MP4
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27.4 MB
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009 Multiple Linear Regression in R.mp4
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MP4
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72.8 MB
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009 Product.txt
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TXT
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139.5 KB
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009 Pruning a tree.mp4
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MP4
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22.2 MB
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009 The Data set for the Regression problem.mp4
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MP4
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41.8 MB
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00_Intro.pdf
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PDF
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334.9 KB
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010 Missing Value imputation in R.mp4
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MP4
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31.7 MB
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010 Pruning a Tree in R.mp4
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MP4
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97 MB
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010 SVM based Regression Model in R.mp4
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MP4
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124 MB
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010 Test-Train split.mp4
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MP4
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48.8 MB
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011 Bias Variance trade-off.mp4
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MP4
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29.4 MB
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011 Seasonality in Data.mp4
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MP4
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20.8 MB
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012 Bi-variate Analysis and Variable Transformation.mp4
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MP4
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113.1 MB
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012 More about test-train split.html
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HTML
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1.4 KB
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013 Test-Train Split in R.mp4
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MP4
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90.9 MB
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013 Variable transformation in R.mp4
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MP4
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67.6 MB
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014 Non Usable Variables.mp4
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MP4
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23.7 MB
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015 Dummy variable creation_ Handling qualitative data.mp4
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MP4
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40.5 MB
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016 Dummy variable creation in R.mp4
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MP4
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52.2 MB
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017 Correlation Matrix and cause-effect relationship.mp4
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MP4
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80.8 MB
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018 Correlation Matrix in R.mp4
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MP4
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94.9 MB
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01_SVM_flow.pdf
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PDF
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143.9 KB
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01_basics.pdf
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PDF
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166 KB
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02_Decision Tree.pdf
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PDF
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205.8 KB
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02_Max_Mar_Class.pdf
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PDF
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287.9 KB
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03_Concepts.pdf
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PDF
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221.7 KB
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03_Max_Mar_Class_LIMIT.pdf
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PDF
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328.7 KB
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04_Stop_condition.pdf
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PDF
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154.8 KB
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04_support_v_class.pdf
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PDF
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189 KB
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05_Prune.pdf
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PDF
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228.5 KB
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05_Support_vec_class_LIMIT.pdf
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PDF
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198.7 KB
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06_Decision Tree - Class.pdf
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PDF
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209.2 KB
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06_SVM.pdf
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PDF
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360.4 KB
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07_Bagging.pdf
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PDF
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303.7 KB
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08_Random_Forest.pdf
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PDF
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168.4 KB
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09_Boosting.pdf
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PDF
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178 KB
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10_Adv_disadv.pdf
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PDF
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145.5 KB
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Downloaded from 1337x.html
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HTML
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512 B
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Movie_classification.csv
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CSV
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54.3 KB
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Movie_regression.csv
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CSV
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53.3 KB
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SVM_R.R
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R
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3 KB
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tree_R.R
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R
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7.5 KB
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