Udemy - Complete Machine Learning with R Studio - ML for 2023 [FC...

Files:

• [CourseClub.Me].url (0.1 KB)
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• 1. Introduction.mp4 (21.2 MB)
• 1. Introduction.srt (2.9 KB)
• 2. Course Resources.html (0.3 KB)

• 1. Linear Discriminant Analysis.mp4 (48.4 MB)
• 1. Linear Discriminant Analysis.srt (12.3 KB)
• 2. Linear Discriminant Analysis in R.mp4 (89.5 MB)
• 2. Linear Discriminant Analysis in R.srt (10.5 KB)

• [CourseClub.Me].url (0.1 KB)
• [FreeCourseSite.com].url (0.1 KB)
• [GigaCourse.Com].url (0.0 KB)

• 1. Test-Train Split.mp4 (45.4 MB)
• 1. Test-Train Split.srt (11.0 KB)
• 2. Test-Train Split in R.mp4 (90.2 MB)
• 2. Test-Train Split in R.srt (10.3 KB)
• 3. K-Nearest Neighbors classifier.mp4 (83.3 MB)
• 3. K-Nearest Neighbors classifier.srt (10.3 KB)
• 4. K-Nearest Neighbors in R.mp4 (79.6 MB)
• 4. K-Nearest Neighbors in R.srt (9.4 KB)
12. Comparing results from 3 models
• 1. Understanding the results of classification models.mp4 (45.8 MB)
• 1. Understanding the results of classification models.srt (7.8 KB)
• 2. Summary of the three models.mp4 (25.1 MB)
• 2. Summary of the three models.srt (6.2 KB)
13. Simple Decision Trees
• 1. Introduction to Decision trees.mp4 (44.8 MB)
• 1. Introduction to Decision trees.srt (4.6 KB)
• 10. Pruning a Tree in R.mp4 (97.0 MB)
• 10. Pruning a Tree in R.srt (11.8 KB)
• 2. Basics of Decision Trees.mp4 (50.6 MB)
• 2. Basics of Decision Trees.srt (13.2 KB)
• 3. Understanding a Regression Tree.mp4 (52.2 MB)
• 3. Understanding a Regression Tree.srt (14.0 KB)
• 4. The stopping criteria for controlling tree growth.mp4 (16.5 MB)
• 4. The stopping criteria for controlling tree growth.srt (4.3 KB)
• 5. Course resources Notes and Datasets.html (0.1 KB)
• 5.1 Files_Dt_r.zip (2.1 MB)
• 6. Importing the Data set into R.mp4 (51.8 MB)
• 6. Importing the Data set into R.srt (8.8 KB)
• 7. Splitting Data into Test and Train Set in R.mp4 (52.6 MB)
• 7. Splitting Data into Test and Train Set in R.srt (7.3 KB)
• 8. Building a Regression Tree in R.mp4 (121.9 MB)
• 8. Building a Regression Tree in R.srt (18.9 KB)
• 9. Pruning a tree.mp4 (22.2 MB)
• 9. Pruning a tree.srt (5.4 KB)
14. Simple Classification Tree
• 1. Classification Trees.mp4 (33.0 MB)
• 1. Classification Trees.srt (8.1 KB)
• 2. The Data set for Classification problem.mp4 (21.9 MB)
• 2. The Data set for Classification problem.srt (2.4 KB)
• 3. Building a classification Tree in R.mp4 (100.1 MB)
• 3. Building a classification Tree in R.srt (11.9 KB)
• 4. Advantages and Disadvantages of Decision Trees.mp4 (7.8 MB)
• 4. Advantages and Disadvantages of Decision Trees.srt (2.2 KB)
15. Ensemble technique 1 - Bagging
• 1. Bagging.mp4 (32.3 MB)
• 1. Bagging.srt (7.6 KB)
• 2. Bagging in R.mp4 (69.3 MB)
• 2. Bagging in R.srt (8.2 KB)
16. Ensemble technique 2 - Random Forest
• 1. Random Forest technique.mp4 (21.4 MB)
• 1. Random Forest technique.srt (5.1 KB)
• 2. Random Forest in R.mp4 (37.4 MB)
• 2. Random Forest in R.srt (5.6 KB)
17. Ensemble technique 3 - GBM, AdaBoost and XGBoost
• 1. Boosting techniques.mp4 (34.4 MB)
• 1. Boosting techniques.srt (9.6 KB)
• 2. Gradient Boosting in R.mp4 (78.6 MB)
• 2. Gradient Boosting in R.srt (9.6 KB)
• 3. AdaBoosting in R.mp4 (103.0 MB)
• 3. AdaBoosting in R.srt (12.2 KB)
• 4. XGBoosting in R.mp4 (186.5 MB)
• 4. XGBoosting in R.srt (21.1 KB)
18. Support Vector Machines
• 1. Introduction to SVM.mp4 (21.6 MB)
• 1. Introduction to SVM.srt (3.2 KB)
• 2. The Concept of a Hyperplane.mp4 (35.3 MB)
• 2. The Concept of a Hyperplane.srt (6.2 KB)
• 3. Maximum Margin Classifier.mp4 (26.2 MB)
• 3. Maximum Margin Classifier.srt (4.4 KB)
• 4. Limitations of Maximum Margin Classifier.mp4 (12.5 MB)
• 4. Limitations of Maximum Margin Classifier.srt (3.1 KB)
19. Support Vector Classifier
• 1. Support Vector classifiers.mp4 (64.1 MB)
• 1. Support Vector classifiers.srt (12.5 KB)
• 2. Limitations of Support Vector Classifiers.mp4 (13.0 MB)
• 2. Limitations of Support Vector Classifiers.srt (1.9 KB)
2. Setting up R Studio and R crash course
• 1. Installing R and R studio.mp4 (40.8 MB)
• 1. Installing R and R studio.srt (7.4 KB)
• 2. This is a milestone!.mp4 (20.7 MB)
• 2. This is a milestone!.srt (3.9 KB)
• 3. Basics of R and R studio.mp4 (48.0 MB)
• 3. Basics of R and R studio.srt (14.4 KB)
• 4. Packages in R.mp4 (98.5 MB)
• 4. Packages in R.srt (14.6 KB)
• 5. Inputting data part 1 Inbuilt datasets of R.mp4 (46.1 MB)
• 5. Inputting data part 1 Inbuilt datasets of R.srt (5.6 KB)
• 6. Inputting data part 2 Manual data entry.mp4 (30.8 MB)
• 6. Inputting data part 2 Manual data entry.srt (3.7 KB)
• 7. Inputting data part 3 Importing from CSV or Text files.mp4 (69.0 MB)
• 7. Inputting data part 3 Importing from CSV or Text files.srt (8.4 KB)
• 7.1 Customer.csv (64.0 KB)
• 7.2 Product.txt (139.5 KB)
• 8. Creating Barplots in R.mp4 (117.2 MB)
• 8. Creating Barplots in R.srt (18.3 KB)
• 9. Creating Histograms in R.mp4 (51.3 MB)
• 9. Creating Histograms in R.srt (7.6 KB)
20. Support Vector Machines
• 1. Kernel Based Support Vector Machines.mp4 (45.7 MB)
• 1. Kernel Based Support Vector Machines.srt (8.5 KB)
• 2. Quiz.html (0.2 KB)
21. Creating Support Vector Machine Model in R
• 1. Course resources Notes and Datasets.html (0.1 KB)
• 1.1 Files_svm_r.zip (1.7 MB)
• 2. Importing and preprocessing data.mp4 (25.0 MB)
• 2. Importing and preprocessing data.srt (2.7 KB)
• 3. Classification SVM model using Linear Kernel.mp4 (166.9 MB)
• 3. Classification SVM model using Linear Kernel.srt (18.4 KB)
• 4. Hyperparameter Tuning for Linear Kernel.mp4 (70.4 MB)
• 4. Hyperparameter Tuning for Linear Kernel.srt (7.2 KB)
• 5. Polynomial Kernel with Hyperparameter Tuning.mp4 (98.7 MB)
• 5. Polynomial Kernel with Hyperparameter Tuning.srt (11.8 KB)
• 6. Radial Kernel with Hyperparameter Tuning.mp4 (67.4 MB)
• 6. Radial Kernel with Hyperparameter Tuning.srt (7.4 KB)
• 7. SVM based Regression Model in R.mp4 (124.0 MB)
• 7. SVM based Regression Model in R.srt (12.7 KB)
22. Congratulations & about your certificate
• 1. The final milestone!.mp4 (11.9 MB)
• 1. The final milestone!.srt (1.8 KB)
• 2. Bonus Lecture.html (2.3 KB)
3. Basics of Statistics
• 1. Types of Data.mp4 (21.8 MB)
• 1. Types of Data.srt (5.2 KB)
• 2. Types of Statistics.mp4 (10.9 MB)
• 2. Types of Statistics.srt (3.3 KB)
• 3. Describing the data graphically.mp4 (65.4 MB)
• 3. Describing the data graphically.srt (13.2 KB)
• 4. Measures of Centers.mp4 (38.6 MB)
• 4. Measures of Centers.srt (8.1 KB)
• 5. Measures of Dispersion.mp4 (22.8 MB)
• 5. Measures of Dispersion.srt (5.3 KB)
4. Intorduction to Machine Learning
• 1. Introduction to Machine Learning.mp4 (123.3 MB)
• 1. Introduction to Machine Learning.srt (19.4 KB)
• 2. Building a Machine Learning Model.mp4 (44.9 MB)
• 2. Building a Machine Learning Model.srt (10.2 KB)
• 3. Quiz Introduction to Machine Learning.html (0.2 KB)
5. Data Preprocessing for Regression Analysis
• 1. Gathering Business Knowledge.mp4 (14.5 MB)
• 1. Gathering Business Knowledge.srt (3.8 KB)
• 10. Missing Value imputation in R.mp4 (31.7 MB)
• 10. Missing Value imputation in R.srt (4.1 KB)
• 11. Seasonality in Data.mp4 (20.8 MB)
• 11. Seasonality in Data.srt (4.1 KB)
• 12. Bi-variate Analysis and Variable Transformation.mp4 (113.1 MB)
• 12. Bi-variate Analysis and Variable Transformation.srt (20.2 KB)
• 13. Variable transformation in R.mp4 (67.6 MB)
• 13. Variable transformation in R.srt (9.3 KB)
• 14. Non Usable Variables.mp4 (23.7 MB)
• 14. Non Usable Variables.srt (6.3 KB)
• 15. Dummy variable creation Handling qualitative data.mp4 (40.5 MB)
• 15. Dummy variable creation Handling qualitative data.srt (5.5 KB)
• 16. Dummy variable creation in R.mp4 (52.2 MB)
• 16. Dummy variable creation in R.srt (6.4 KB)
• 17. Correlation Matrix and cause-effect relationship.mp4 (80.8 MB)
• 17. Correlation Matrix and cause-effect relationship.srt (11.4 KB)
• 18. Correlation Matrix in R.mp4 (94.9 MB)
• 18. Correlation Matrix in R.srt (7.2 KB)
• 19. Quiz.html (0.2 KB)
• 2. Data Exploration.mp4 (20.1 MB)
• 2. Data Exploration.srt (3.8 KB)
• 3. The Data and the Data Dictionary.mp4 (78.3 MB)
• 3. The Data and the Data Dictionary.srt (8.5 KB)
• 4. Importing the dataset into R.mp4 (15.9 MB)
• 4. Importing the dataset into R.srt (2.9 KB)
• 5. Univariate Analysis and EDD.mp4 (27.2 MB)
• 5. Univariate Analysis and EDD.srt (3.8 KB)
• 6. EDD in R.mp4 (112.0 MB)
• 6. EDD in R.srt (13.7 KB)
• 7. Outlier Treatment.mp4 (27.3 MB)
• 7. Outlier Treatment.srt (4.9 KB)
• 8. Outlier Treatment in R.mp4 (37.8 MB)
• 8. Outlier Treatment in R.srt (4.9 KB)
• 9. Missing Value imputation.mp4 (23.2 MB)
• 9. Missing Value imputation.srt (4.2 KB)
6. Linear Regression Model
• 1. The problem statement.mp4 (10.6 MB)
• 1. The problem statement.srt (1.8 KB)
• 10. Quiz.html (0.2 KB)
• 11. Test-Train split.mp4 (48.8 MB)
• 11. Test-Train split.srt (12.6 KB)
• 12. Bias Variance trade-off.mp4 (29.4 MB)
• 12. Bias Variance trade-off.srt (8.2 KB)
• 13. More about test-train split.html (0.5 KB)
• 14. Test-Train Split in R.mp4 (90.9 MB)
• 14. Test-Train Split in R.srt (9.6 KB)
• 15. Assignment 1 Regression Analysis.html (0.2 KB)
• 2. Basic equations and Ordinary Least Squared (OLS) method.mp4 (49.9 MB)
• 2. Basic equations and Ordinary Least Squared (OLS) method.srt (12.7 KB)
• 3. Assessing Accuracy of predicted coefficients.mp4 (103.9 MB)
• 3. Assessing Accuracy of predicted coefficients.srt (19.9 KB)
• 4. Assessing Model Accuracy - RSE and R squared.mp4 (49.5 MB)
• 4. Assessing Model Accuracy - RSE and R squared.srt (9.8 KB)
• 5. Simple Linear Regression in R.mp4 (50.5 MB)
• 5. Simple Linear Regression in R.srt (9.6 KB)
• 6. Multiple Linear Regression.mp4 (38.7 MB)
• 6. Multiple Linear Regression.srt (7.4 KB)
• 7. The F - statistic.mp4 (63.8 MB)
• 7. The F - statistic.srt (11.5 KB)
• 8. Interpreting result for categorical Variable.mp4 (26.9 MB)
• 8. Interpreting result for categorical Variable.srt (6.9 KB)
• 9. Multiple Linear Regression in R.mp4 (72.8 MB)
• 9. Multiple Linear Regression in R.srt (9.6 KB)
7. Regression models other than OLS
• 1. Linear models other than OLS.mp4 (19.0 MB)
• 1. Linear models other than OLS.srt (5.3 KB)
• 2. Subset Selection techniques.mp4 (86.7 MB)
• 2. Subset Selection techniques.srt (15.3 KB)
• 3. Subset selection in R.mp4 (76.6 MB)
• 3. Subset selection in R.srt (8.4 KB)
• 4. Shrinkage methods - Ridge Regression and The Lasso.mp4 (38.4 MB)
• 4. Shrinkage methods - Ridge Regression and The Lasso.srt (9.4 KB)
• 5. Ridge regression and Lasso in R.mp4 (124.0 MB)
• 5. Ridge regression and Lasso in R.srt (13.0 KB)
8. Introduction to the classification Models
• 1. Three classification models and Data set.mp4 (52.3 MB)
• 1. Three classification models and Data set.srt (6.7 KB)
• 1.1 Classification preprocessed data R.csv (41.0 KB)
• 2. Importing the data into R.mp4 (8.8 MB)
• 2. Importing the data into R.srt (1.4 KB)
• 2.1 Classification preprocessed data R.csv (51.0 KB)
• 3. The problem statements.mp4 (17.1 MB)
• 3. The problem statements.srt (1.8 KB)
• 4. Why can't we use Linear Regression.mp4 (20.3 MB)
• 4. Why can't we use Linear Regression.srt (5.7 KB)
9. Logistic Regression
• 1. Logistic Regression.mp4 (38.8 MB)
• 1. Logistic Regression.srt (8.9 KB)
• 2. Training a Simple Logistic model in R.mp4 (31.0 MB)
• 2. Training a Simple Logistic model in R.srt (4.3 KB)
• 3. Results of Simple Logistic Regression.mp4 (30.9 MB)
• 3. Results of Simple Logistic Regression.srt (6.1 KB)
• 4. Logistic with multiple predictors.mp4 (10.0 MB)
• 4. Logistic with multiple predictors.srt (3.1 KB)
• 5. Training multiple predictor Logistic model in R.mp4 (18.3 MB)
• 5. Training multiple predictor Logistic model in R.srt (2.1 KB)
• 6. Confusion Matrix.mp4 (26.6 MB)
• 6. Confusion Matrix.srt (5.2 KB)
• 7. Evaluating Model performance.mp4 (42.5 MB)
• 7. Evaluating Model performance.srt (9.7 KB)
• 8. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 (66.1 MB)
• 8. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt (7.7 KB)
• 9. Quiz.html (0.2 KB)