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Udemy Complete Machine Learning with R Studio ML for 2021 Giga Course

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Files:

[GigaCourse.Com].url 49 bytes
001 Introduction.mp4 21.2 MB
002 Course Resources.html 1.2 KB
001 Installing R and R studio.mp4 40.8 MB
002 This is a milestone!.mp4 20.7 MB
003 Basics of R and R studio.mp4 48.0 MB
004 Packages in R.mp4 98.5 MB
005 Inputting data part 1_ Inbuilt datasets of R.mp4 46.1 MB
006 Inputting data part 2_ Manual data entry.mp4 30.8 MB
007 Inputting data part 3_ Importing from CSV or Text files.mp4 69.0 MB
008 Creating Barplots in R.mp4 117.2 MB
009 Creating Histograms in R.mp4 51.3 MB
009 Customer.csv 64.0 KB
009 Product.txt 137.7 KB
001 Types of Data.mp4 21.8 MB
002 Types of Statistics.mp4 10.9 MB
003 Describing the data graphically.mp4 65.4 MB
004 Measures of Centers.mp4 38.5 MB
005 Measures of Dispersion.mp4 22.8 MB
001 Introduction to Machine Learning.mp4 123.3 MB
002 Building a Machine Learning Model.mp4 44.9 MB
001 Gathering Business Knowledge.mp4 25.0 MB
002 Data Exploration.mp4 23.3 MB
003 The Data and the Data Dictionary.mp4 78.3 MB
004 Importing the dataset into R.mp4 15.9 MB
005 Univariate Analysis and EDD.mp4 27.2 MB
006 EDD in R.mp4 112.0 MB
007 Outlier Treatment.mp4 27.7 MB
008 Outlier Treatment in R.mp4 37.8 MB
009 Missing Value imputation.mp4 27.4 MB
010 Missing Value imputation in R.mp4 31.7 MB
011 Seasonality in Data.mp4 20.8 MB
012 Bi-variate Analysis and Variable Transformation.mp4 113.1 MB
013 Variable transformation in R.mp4 67.6 MB
014 Non Usable Variables.mp4 23.7 MB
015 Dummy variable creation_ Handling qualitative data.mp4 40.5 MB
016 Dummy variable creation in R.mp4 52.2 MB
017 Correlation Matrix and cause-effect relationship.mp4 80.8 MB
018 Correlation Matrix in R.mp4 94.9 MB
[GigaCourse.Com].url 49 bytes
001 The problem statement.mp4 10.6 MB
002 Basic equations and Ordinary Least Squared (OLS) method.mp4 49.9 MB
003 Assessing Accuracy of predicted coefficients.mp4 103.9 MB
004 Assessing Model Accuracy - RSE and R squared.mp4 49.5 MB
005 Simple Linear Regression in R.mp4 50.5 MB
006 Multiple Linear Regression.mp4 38.7 MB
007 The F - statistic.mp4 63.8 MB
008 Interpreting result for categorical Variable.mp4 26.9 MB
009 Multiple Linear Regression in R.mp4 72.8 MB
010 Test-Train split.mp4 48.8 MB
011 Bias Variance trade-off.mp4 29.4 MB
012 More about test-train split.html 1.4 KB
013 Test-Train Split in R.mp4 90.9 MB
001 Linear models other than OLS.mp4 19.0 MB
002 Subset Selection techniques.mp4 86.7 MB
003 Subset selection in R.mp4 76.6 MB
004 Shrinkage methods - Ridge Regression and The Lasso.mp4 38.4 MB
005 Ridge regression and Lasso in R.mp4 124.0 MB
001 The Data and the Data Dictionary.mp4 87.4 MB
002 Importing the dataset into R.mp4 16.3 MB
003 EDD in R.mp4 77.8 MB
004 Outlier Treatment in R.mp4 31.2 MB
005 Missing Value imputation in R.mp4 23.4 MB
006 Variable transformation in R.mp4 46.5 MB
007 Dummy variable creation in R.mp4 52.5 MB
001 Three Classifiers and the problem statement.mp4 22.8 MB
002 Why can't we use Linear Regression_.mp4 20.2 MB
001 Logistic Regression.mp4 38.8 MB
002 Training a Simple Logistic model in R.mp4 31.0 MB
003 Results of Simple Logistic Regression.mp4 30.9 MB
004 Logistic with multiple predictors.mp4 9.9 MB
005 Training multiple predictor Logistic model in R.mp4 18.3 MB
006 Confusion Matrix.mp4 26.6 MB
007 Evaluating Model performance.mp4 42.5 MB
008 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 66.1 MB
[GigaCourse.Com].url 49 bytes
001 Linear Discriminant Analysis.mp4 48.4 MB
002 Linear Discriminant Analysis in R.mp4 89.5 MB
001 Test-Train Split.mp4 45.4 MB
002 Test-Train Split in R.mp4 90.2 MB
003 K-Nearest Neighbors classifier.mp4 83.3 MB
004 K-Nearest Neighbors in R.mp4 79.6 MB
001 Understanding the results of classification models.mp4 45.8 MB
002 Summary of the three models.mp4 25.1 MB
001 Basics of Decision Trees.mp4 50.6 MB
002 Understanding a Regression Tree.mp4 52.2 MB
003 The stopping criteria for controlling tree growth.mp4 16.5 MB
004 The Data set for this part.mp4 42.0 MB
005 Course resources_ Notes and Datasets.html 990 bytes
006 Importing the Data set into R.mp4 51.8 MB
007 Splitting Data into Test and Train Set in R.mp4 52.6 MB
008 Building a Regression Tree in R.mp4 121.9 MB
009 Pruning a tree.mp4 22.2 MB
010 Pruning a Tree in R.mp4 97.0 MB
00_Intro.pdf 334.9 KB
01_basics.pdf 166.0 KB
02_Decision Tree.pdf 205.8 KB
03_Concepts.pdf 221.7 KB
04_Stop_condition.pdf 154.8 KB
05_Prune.pdf 228.5 KB
06_Decision Tree - Class.pdf 209.2 KB
07_Bagging.pdf 303.7 KB
08_Random_Forest.pdf 168.4 KB
09_Boosting.pdf 178.0 KB
10_Adv_disadv.pdf 145.5 KB
Movie_classification.csv 54.3 KB
Movie_regression.csv 53.3 KB
tree_R.R 7.5 KB
001 Classification Trees.mp4 33.0 MB
002 The Data set for Classification problem.mp4 21.9 MB
003 Building a classification Tree in R.mp4 100.1 MB
004 Advantages and Disadvantages of Decision Trees.mp4 7.8 MB
001 Bagging.mp4 32.3 MB
002 Bagging in R.mp4 69.3 MB
001 Random Forest technique.mp4 21.4 MB
002 Random Forest in R.mp4 37.4 MB
[GigaCourse.Com].url 49 bytes
001 Boosting techniques.mp4 34.4 MB
002 Gradient Boosting in R.mp4 78.6 MB
003 AdaBoosting in R.mp4 103.0 MB
004 XGBoosting in R.mp4 186.5 MB
001 Content flow.mp4 9.8 MB
002 The Concept of a Hyperplane.mp4 35.3 MB
003 Maximum Margin Classifier.mp4 26.2 MB
004 Limitations of Maximum Margin Classifier.mp4 12.5 MB
001 Support Vector classifiers.mp4 64.1 MB
002 Limitations of Support Vector Classifiers.mp4 13.0 MB
001 Kernel Based Support Vector Machines.mp4 45.7 MB
001 The Data set for the Classification problem.mp4 22.0 MB
002 Course resources_ Notes and Datasets.html 963 bytes
003 Importing Data into R.mp4 65.3 MB
004 Test-Train Split.mp4 59.4 MB
005 Classification SVM model using Linear Kernel.mp4 166.9 MB
006 Hyperparameter Tuning for Linear Kernel.mp4 70.4 MB
007 Polynomial Kernel with Hyperparameter Tuning.mp4 98.7 MB
008 Radial Kernel with Hyperparameter Tuning.mp4 67.4 MB
009 The Data set for the Regression problem.mp4 41.8 MB
010 SVM based Regression Model in R.mp4 124.0 MB
00000_Intro.pdf 334.9 KB
01_SVM_flow.pdf 143.9 KB
02_Max_Mar_Class.pdf 287.9 KB
03_Max_Mar_Class_LIMIT.pdf 328.7 KB
04_support_v_class.pdf 189.0 KB
05_Support_vec_class_LIMIT.pdf 198.7 KB
06_SVM.pdf 360.4 KB
Movie_classification.csv 54.3 KB
Movie_regression.csv 53.3 KB
SVM_R.R 3.0 KB
[GigaCourse.Com].url 49 bytes
001 The final milestone!.mp4 11.9 MB
002 Congratulations & About your certificate.html 2.7 KB
[GigaCourse.Com].url 49 bytes

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