Udemy Complete Machine Learning with R Studio ML for 2021 Giga Course
Seeders : 0 Leechers : 2
| Torrent Hash : | 0D002469C6F8295EC8B03A22F6A0B53029B0AF10 |
| Torrent Added : | at Oct. 25, 2023, 7:49 p.m. in Other |
| Torrent Size : | 5.9 GB |
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Udemy Complete Machine Learning with R Studio ML for 2021 Giga Course
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Udemy Complete Machine Learning with R Studio ML for 2021 Giga Course
[GigaCourse.Com].url -
001 Introduction.mp4 -
002 Course Resources.html -
001 Installing R and R studio.mp4 -
002 This is a milestone!.mp4 -
003 Basics of R and R studio.mp4 -
004 Packages in R.mp4 -
005 Inputting data part 1_ Inbuilt datasets of R.mp4 -
006 Inputting data part 2_ Manual data entry.mp4 -
007 Inputting data part 3_ Importing from CSV or Text files.mp4 -
008 Creating Barplots in R.mp4 -
009 Creating Histograms in R.mp4 -
009 Customer.csv -
009 Product.txt -
001 Types of Data.mp4 -
002 Types of Statistics.mp4 -
003 Describing the data graphically.mp4 -
004 Measures of Centers.mp4 -
005 Measures of Dispersion.mp4 -
001 Introduction to Machine Learning.mp4 -
002 Building a Machine Learning Model.mp4 -
001 Gathering Business Knowledge.mp4 -
002 Data Exploration.mp4 -
003 The Data and the Data Dictionary.mp4 -
004 Importing the dataset into R.mp4 -
005 Univariate Analysis and EDD.mp4 -
006 EDD in R.mp4 -
007 Outlier Treatment.mp4 -
008 Outlier Treatment in R.mp4 -
009 Missing Value imputation.mp4 -
010 Missing Value imputation in R.mp4 -
011 Seasonality in Data.mp4 -
012 Bi-variate Analysis and Variable Transformation.mp4 -
013 Variable transformation in R.mp4 -
014 Non Usable Variables.mp4 -
015 Dummy variable creation_ Handling qualitative data.mp4 -
016 Dummy variable creation in R.mp4 -
017 Correlation Matrix and cause-effect relationship.mp4 -
018 Correlation Matrix in R.mp4 -
[GigaCourse.Com].url -
001 The problem statement.mp4 -
002 Basic equations and Ordinary Least Squared (OLS) method.mp4 -
003 Assessing Accuracy of predicted coefficients.mp4 -
004 Assessing Model Accuracy - RSE and R squared.mp4 -
005 Simple Linear Regression in R.mp4 -
006 Multiple Linear Regression.mp4 -
007 The F - statistic.mp4 -
008 Interpreting result for categorical Variable.mp4 -
009 Multiple Linear Regression in R.mp4 -
010 Test-Train split.mp4 -
011 Bias Variance trade-off.mp4 -
012 More about test-train split.html -
013 Test-Train Split in R.mp4 -
001 Linear models other than OLS.mp4 -
002 Subset Selection techniques.mp4 -
003 Subset selection in R.mp4 -
004 Shrinkage methods - Ridge Regression and The Lasso.mp4 -
005 Ridge regression and Lasso in R.mp4 -
001 The Data and the Data Dictionary.mp4 -
002 Importing the dataset into R.mp4 -
003 EDD in R.mp4 -
004 Outlier Treatment in R.mp4 -
005 Missing Value imputation in R.mp4 -
006 Variable transformation in R.mp4 -
007 Dummy variable creation in R.mp4 -
001 Three Classifiers and the problem statement.mp4 -
002 Why can't we use Linear Regression_.mp4 -
001 Logistic Regression.mp4 -
002 Training a Simple Logistic model in R.mp4 -
003 Results of Simple Logistic Regression.mp4 -
004 Logistic with multiple predictors.mp4 -
005 Training multiple predictor Logistic model in R.mp4 -
006 Confusion Matrix.mp4 -
007 Evaluating Model performance.mp4 -
008 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 -
[GigaCourse.Com].url -
001 Linear Discriminant Analysis.mp4 -
002 Linear Discriminant Analysis in R.mp4 -
001 Test-Train Split.mp4 -
002 Test-Train Split in R.mp4 -
003 K-Nearest Neighbors classifier.mp4 -
004 K-Nearest Neighbors in R.mp4 -
001 Understanding the results of classification models.mp4 -
002 Summary of the three models.mp4 -
001 Basics of Decision Trees.mp4 -
002 Understanding a Regression Tree.mp4 -
003 The stopping criteria for controlling tree growth.mp4 -
004 The Data set for this part.mp4 -
005 Course resources_ Notes and Datasets.html -
006 Importing the Data set into R.mp4 -
007 Splitting Data into Test and Train Set in R.mp4 -
008 Building a Regression Tree in R.mp4 -
009 Pruning a tree.mp4 -
010 Pruning a Tree in R.mp4 -
00_Intro.pdf -
01_basics.pdf -
02_Decision Tree.pdf -
03_Concepts.pdf -
04_Stop_condition.pdf -
05_Prune.pdf -
06_Decision Tree - Class.pdf -
07_Bagging.pdf -
08_Random_Forest.pdf -
09_Boosting.pdf -
10_Adv_disadv.pdf -
Movie_classification.csv -
Movie_regression.csv -
tree_R.R -
001 Classification Trees.mp4 -
002 The Data set for Classification problem.mp4 -
003 Building a classification Tree in R.mp4 -
004 Advantages and Disadvantages of Decision Trees.mp4 -
001 Bagging.mp4 -
002 Bagging in R.mp4 -
001 Random Forest technique.mp4 -
002 Random Forest in R.mp4 -
[GigaCourse.Com].url -
001 Boosting techniques.mp4 -
002 Gradient Boosting in R.mp4 -
003 AdaBoosting in R.mp4 -
004 XGBoosting in R.mp4 -
001 Content flow.mp4 -
002 The Concept of a Hyperplane.mp4 -
003 Maximum Margin Classifier.mp4 -
004 Limitations of Maximum Margin Classifier.mp4 -
001 Support Vector classifiers.mp4 -
002 Limitations of Support Vector Classifiers.mp4 -
001 Kernel Based Support Vector Machines.mp4 -
001 The Data set for the Classification problem.mp4 -
002 Course resources_ Notes and Datasets.html -
003 Importing Data into R.mp4 -
004 Test-Train Split.mp4 -
005 Classification SVM model using Linear Kernel.mp4 -
006 Hyperparameter Tuning for Linear Kernel.mp4 -
007 Polynomial Kernel with Hyperparameter Tuning.mp4 -
008 Radial Kernel with Hyperparameter Tuning.mp4 -
009 The Data set for the Regression problem.mp4 -
010 SVM based Regression Model in R.mp4 -
00000_Intro.pdf -
01_SVM_flow.pdf -
02_Max_Mar_Class.pdf -
03_Max_Mar_Class_LIMIT.pdf -
04_support_v_class.pdf -
05_Support_vec_class_LIMIT.pdf -
06_SVM.pdf -
Movie_classification.csv -
Movie_regression.csv -
SVM_R.R -
[GigaCourse.Com].url -
001 The final milestone!.mp4 -
002 Congratulations & About your certificate.html -
[GigaCourse.Com].url -
Please login or create a FREE account to post comments
[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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