[FreeCourseSite.com] Udemy - Complete Machine Learning with R Studio - ML for 2023

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[FreeCourseSite.com] Udemy - Complete Machine Learning with R Studio - ML for 2023

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种子哈希：c6d7044beb36d6ef59890b0fdea52f71a30c9bab
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收录时间：2023-12-27
最近下载：2025-09-23

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文件列表

  • 17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/4. XGBoosting in R.mp4 195.5 MB
  • 21. Creating Support Vector Machine Model in R/3. Classification SVM model using Linear Kernel.mp4 175.1 MB
  • 7. Regression models other than OLS/5. Ridge regression and Lasso in R.mp4 130.0 MB
  • 21. Creating Support Vector Machine Model in R/7. SVM based Regression Model in R.mp4 130.0 MB
  • 4. Intorduction to Machine Learning/1. Introduction to Machine Learning.mp4 129.3 MB
  • 13. Simple Decision Trees/8. Building a Regression Tree in R.mp4 127.8 MB
  • 2. Setting up R Studio and R crash course/8. Creating Barplots in R.mp4 122.9 MB
  • 5. Data Preprocessing for Regression Analysis/12. Bi-variate Analysis and Variable Transformation.mp4 118.6 MB
  • 5. Data Preprocessing for Regression Analysis/6. EDD in R.mp4 117.4 MB
  • 6. Linear Regression Model/3. Assessing Accuracy of predicted coefficients.mp4 108.9 MB
  • 17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/3. AdaBoosting in R.mp4 108.0 MB
  • 14. Simple Classification Tree/3. Building a classification Tree in R.mp4 105.0 MB
  • 21. Creating Support Vector Machine Model in R/5. Polynomial Kernel with Hyperparameter Tuning.mp4 103.4 MB
  • 2. Setting up R Studio and R crash course/4. Packages in R.mp4 103.3 MB
  • 13. Simple Decision Trees/10. Pruning a Tree in R.mp4 101.7 MB
  • 5. Data Preprocessing for Regression Analysis/18. Correlation Matrix in R.mp4 99.6 MB
  • 6. Linear Regression Model/14. Test-Train Split in R.mp4 95.3 MB
  • 11. K-Nearest Neighbors/2. Test-Train Split in R.mp4 94.5 MB
  • 10. Linear Discriminant Analysis/2. Linear Discriminant Analysis in R.mp4 93.8 MB
  • 7. Regression models other than OLS/2. Subset Selection techniques.mp4 90.9 MB
  • 11. K-Nearest Neighbors/3. K-Nearest Neighbors classifier.mp4 87.3 MB
  • 5. Data Preprocessing for Regression Analysis/17. Correlation Matrix and cause-effect relationship.mp4 84.8 MB
  • 11. K-Nearest Neighbors/4. K-Nearest Neighbors in R.mp4 83.5 MB
  • 17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/2. Gradient Boosting in R.mp4 82.4 MB
  • 5. Data Preprocessing for Regression Analysis/3. The Data and the Data Dictionary.mp4 82.1 MB
  • 7. Regression models other than OLS/3. Subset selection in R.mp4 80.3 MB
  • 6. Linear Regression Model/9. Multiple Linear Regression in R.mp4 76.4 MB
  • 21. Creating Support Vector Machine Model in R/4. Hyperparameter Tuning for Linear Kernel.mp4 73.9 MB
  • 15. Ensemble technique 1 - Bagging/2. Bagging in R.mp4 72.7 MB
  • 2. Setting up R Studio and R crash course/7. Inputting data part 3 Importing from CSV or Text files.mp4 72.3 MB
  • 5. Data Preprocessing for Regression Analysis/13. Variable transformation in R.mp4 70.9 MB
  • 21. Creating Support Vector Machine Model in R/6. Radial Kernel with Hyperparameter Tuning.mp4 70.6 MB
  • 9. Logistic Regression/8. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 69.3 MB
  • 3. Basics of Statistics/3. Describing the data graphically.mp4 68.5 MB
  • 19. Support Vector Classifier/1. Support Vector classifiers.mp4 67.2 MB
  • 6. Linear Regression Model/7. The F - statistic.mp4 66.9 MB
  • 13. Simple Decision Trees/7. Splitting Data into Test and Train Set in R.mp4 55.1 MB
  • 8. Introduction to the classification Models/1. Three classification models and Data set.mp4 54.9 MB
  • 5. Data Preprocessing for Regression Analysis/16. Dummy variable creation in R.mp4 54.8 MB
  • 13. Simple Decision Trees/3. Understanding a Regression Tree.mp4 54.7 MB
  • 13. Simple Decision Trees/6. Importing the Data set into R.mp4 54.4 MB
  • 2. Setting up R Studio and R crash course/9. Creating Histograms in R.mp4 53.8 MB
  • 13. Simple Decision Trees/2. Basics of Decision Trees.mp4 53.0 MB
  • 6. Linear Regression Model/5. Simple Linear Regression in R.mp4 52.9 MB
  • 6. Linear Regression Model/2. Basic equations and Ordinary Least Squared (OLS) method.mp4 52.3 MB
  • 6. Linear Regression Model/4. Assessing Model Accuracy - RSE and R squared.mp4 51.9 MB
  • 6. Linear Regression Model/11. Test-Train split.mp4 51.1 MB
  • 10. Linear Discriminant Analysis/1. Linear Discriminant Analysis.mp4 50.7 MB
  • 2. Setting up R Studio and R crash course/3. Basics of R and R studio.mp4 50.3 MB
  • 2. Setting up R Studio and R crash course/5. Inputting data part 1 Inbuilt datasets of R.mp4 48.4 MB
  • 12. Comparing results from 3 models/1. Understanding the results of classification models.mp4 48.0 MB
  • 20. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4 47.9 MB
  • 11. K-Nearest Neighbors/1. Test-Train Split.mp4 47.6 MB
  • 4. Intorduction to Machine Learning/2. Building a Machine Learning Model.mp4 47.1 MB
  • 13. Simple Decision Trees/1. Introduction to Decision trees.mp4 46.9 MB
  • 9. Logistic Regression/7. Evaluating Model performance.mp4 44.6 MB
  • 2. Setting up R Studio and R crash course/1. Installing R and R studio.mp4 42.8 MB
  • 5. Data Preprocessing for Regression Analysis/15. Dummy variable creation Handling qualitative data.mp4 42.5 MB
  • 9. Logistic Regression/1. Logistic Regression.mp4 40.7 MB
  • 6. Linear Regression Model/6. Multiple Linear Regression.mp4 40.6 MB
  • 3. Basics of Statistics/4. Measures of Centers.mp4 40.4 MB
  • 7. Regression models other than OLS/4. Shrinkage methods - Ridge Regression and The Lasso.mp4 40.3 MB
  • 5. Data Preprocessing for Regression Analysis/8. Outlier Treatment in R.mp4 39.7 MB
  • 16. Ensemble technique 2 - Random Forest/2. Random Forest in R.mp4 39.3 MB
  • 18. Support Vector Machines/2. The Concept of a Hyperplane.mp4 37.1 MB
  • 17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/1. Boosting techniques.mp4 36.0 MB
  • 14. Simple Classification Tree/1. Classification Trees.mp4 34.6 MB
  • 15. Ensemble technique 1 - Bagging/1. Bagging.mp4 33.9 MB
  • 5. Data Preprocessing for Regression Analysis/10. Missing Value imputation in R.mp4 33.2 MB
  • 9. Logistic Regression/2. Training a Simple Logistic model in R.mp4 32.5 MB
  • 9. Logistic Regression/3. Results of Simple Logistic Regression.mp4 32.4 MB
  • 2. Setting up R Studio and R crash course/6. Inputting data part 2 Manual data entry.mp4 32.3 MB
  • 6. Linear Regression Model/12. Bias Variance trade-off.mp4 30.8 MB
  • 5. Data Preprocessing for Regression Analysis/7. Outlier Treatment.mp4 28.6 MB
  • 5. Data Preprocessing for Regression Analysis/5. Univariate Analysis and EDD.mp4 28.5 MB
  • 6. Linear Regression Model/8. Interpreting result for categorical Variable.mp4 28.3 MB
  • 9. Logistic Regression/6. Confusion Matrix.mp4 27.8 MB
  • 18. Support Vector Machines/3. Maximum Margin Classifier.mp4 27.4 MB
  • 12. Comparing results from 3 models/2. Summary of the three models.mp4 26.3 MB
  • 21. Creating Support Vector Machine Model in R/2. Importing and preprocessing data.mp4 26.2 MB
  • 5. Data Preprocessing for Regression Analysis/14. Non Usable Variables.mp4 24.9 MB
  • 5. Data Preprocessing for Regression Analysis/9. Missing Value imputation.mp4 24.3 MB
  • 3. Basics of Statistics/5. Measures of Dispersion.mp4 24.0 MB
  • 13. Simple Decision Trees/9. Pruning a tree.mp4 23.3 MB
  • 14. Simple Classification Tree/2. The Data set for Classification problem.mp4 23.0 MB
  • 3. Basics of Statistics/1. Types of Data.mp4 22.8 MB
  • 18. Support Vector Machines/1. Introduction to SVM.mp4 22.7 MB
  • 16. Ensemble technique 2 - Random Forest/1. Random Forest technique.mp4 22.5 MB
  • 1. Welcome to the course/1. Introduction.mp4 22.2 MB
  • 5. Data Preprocessing for Regression Analysis/11. Seasonality in Data.mp4 21.8 MB
  • 2. Setting up R Studio and R crash course/2. This is a milestone!.mp4 21.7 MB
  • 8. Introduction to the classification Models/4. Why can't we use Linear Regression.mp4 21.2 MB
  • 5. Data Preprocessing for Regression Analysis/2. Data Exploration.mp4 21.1 MB
  • 7. Regression models other than OLS/1. Linear models other than OLS.mp4 19.9 MB
  • 9. Logistic Regression/5. Training multiple predictor Logistic model in R.mp4 19.2 MB
  • 8. Introduction to the classification Models/3. The problem statements.mp4 17.9 MB
  • 13. Simple Decision Trees/4. The stopping criteria for controlling tree growth.mp4 17.3 MB
  • 5. Data Preprocessing for Regression Analysis/4. Importing the dataset into R.mp4 16.7 MB
  • 5. Data Preprocessing for Regression Analysis/1. Gathering Business Knowledge.mp4 15.2 MB
  • 19. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp4 13.6 MB
  • 18. Support Vector Machines/4. Limitations of Maximum Margin Classifier.mp4 13.1 MB
  • 22. Congratulations & about your certificate/1. The final milestone!.mp4 12.4 MB
  • 3. Basics of Statistics/2. Types of Statistics.mp4 11.5 MB
  • 6. Linear Regression Model/1. The problem statement.mp4 11.2 MB
  • 9. Logistic Regression/4. Logistic with multiple predictors.mp4 10.4 MB
  • 8. Introduction to the classification Models/2. Importing the data into R.mp4 9.2 MB
  • 14. Simple Classification Tree/4. Advantages and Disadvantages of Decision Trees.mp4 8.1 MB
  • 13. Simple Decision Trees/5.1 Files_Dt_r.zip 2.2 MB
  • 21. Creating Support Vector Machine Model in R/1.1 Files_svm_r.zip 1.8 MB
  • 2. Setting up R Studio and R crash course/7.2 Product.txt 142.8 kB
  • 2. Setting up R Studio and R crash course/7.1 Customer.csv 65.6 kB
  • 8. Introduction to the classification Models/2.1 Classification preprocessed data R.csv 52.2 kB
  • 8. Introduction to the classification Models/1.1 Classification preprocessed data R.csv 42.0 kB
  • 17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/4. XGBoosting in R.srt 21.6 kB
  • 5. Data Preprocessing for Regression Analysis/12. Bi-variate Analysis and Variable Transformation.srt 20.7 kB
  • 6. Linear Regression Model/3. Assessing Accuracy of predicted coefficients.srt 20.4 kB
  • 4. Intorduction to Machine Learning/1. Introduction to Machine Learning.srt 19.8 kB
  • 13. Simple Decision Trees/8. Building a Regression Tree in R.srt 19.3 kB
  • 21. Creating Support Vector Machine Model in R/3. Classification SVM model using Linear Kernel.srt 18.8 kB
  • 2. Setting up R Studio and R crash course/8. Creating Barplots in R.srt 18.8 kB
  • 7. Regression models other than OLS/2. Subset Selection techniques.srt 15.6 kB
  • 2. Setting up R Studio and R crash course/4. Packages in R.srt 14.9 kB
  • 2. Setting up R Studio and R crash course/3. Basics of R and R studio.srt 14.7 kB
  • 13. Simple Decision Trees/3. Understanding a Regression Tree.srt 14.3 kB
  • 5. Data Preprocessing for Regression Analysis/6. EDD in R.srt 14.1 kB
  • 3. Basics of Statistics/3. Describing the data graphically.srt 13.5 kB
  • 13. Simple Decision Trees/2. Basics of Decision Trees.srt 13.5 kB
  • 7. Regression models other than OLS/5. Ridge regression and Lasso in R.srt 13.3 kB
  • 21. Creating Support Vector Machine Model in R/7. SVM based Regression Model in R.srt 13.0 kB
  • 6. Linear Regression Model/2. Basic equations and Ordinary Least Squared (OLS) method.srt 13.0 kB
  • 6. Linear Regression Model/11. Test-Train split.srt 12.9 kB
  • 19. Support Vector Classifier/1. Support Vector classifiers.srt 12.8 kB
  • 10. Linear Discriminant Analysis/1. Linear Discriminant Analysis.srt 12.6 kB
  • 17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/3. AdaBoosting in R.srt 12.5 kB
  • 14. Simple Classification Tree/3. Building a classification Tree in R.srt 12.2 kB
  • 21. Creating Support Vector Machine Model in R/5. Polynomial Kernel with Hyperparameter Tuning.srt 12.1 kB
  • 13. Simple Decision Trees/10. Pruning a Tree in R.srt 12.1 kB
  • 6. Linear Regression Model/7. The F - statistic.srt 11.7 kB
  • 5. Data Preprocessing for Regression Analysis/17. Correlation Matrix and cause-effect relationship.srt 11.7 kB
  • 11. K-Nearest Neighbors/1. Test-Train Split.srt 11.2 kB
  • 10. Linear Discriminant Analysis/2. Linear Discriminant Analysis in R.srt 10.7 kB
  • 11. K-Nearest Neighbors/3. K-Nearest Neighbors classifier.srt 10.6 kB
  • 11. K-Nearest Neighbors/2. Test-Train Split in R.srt 10.5 kB
  • 4. Intorduction to Machine Learning/2. Building a Machine Learning Model.srt 10.5 kB
  • 6. Linear Regression Model/4. Assessing Model Accuracy - RSE and R squared.srt 10.0 kB
  • 9. Logistic Regression/7. Evaluating Model performance.srt 9.9 kB
  • 17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/2. Gradient Boosting in R.srt 9.8 kB
  • 6. Linear Regression Model/14. Test-Train Split in R.srt 9.8 kB
  • 17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/1. Boosting techniques.srt 9.8 kB
  • 6. Linear Regression Model/9. Multiple Linear Regression in R.srt 9.8 kB
  • 6. Linear Regression Model/5. Simple Linear Regression in R.srt 9.8 kB
  • 7. Regression models other than OLS/4. Shrinkage methods - Ridge Regression and The Lasso.srt 9.6 kB
  • 11. K-Nearest Neighbors/4. K-Nearest Neighbors in R.srt 9.6 kB
  • 5. Data Preprocessing for Regression Analysis/13. Variable transformation in R.srt 9.5 kB
  • 9. Logistic Regression/1. Logistic Regression.srt 9.1 kB
  • 13. Simple Decision Trees/6. Importing the Data set into R.srt 9.0 kB
  • 5. Data Preprocessing for Regression Analysis/3. The Data and the Data Dictionary.srt 8.7 kB
  • 20. Support Vector Machines/1. Kernel Based Support Vector Machines.srt 8.7 kB
  • 2. Setting up R Studio and R crash course/7. Inputting data part 3 Importing from CSV or Text files.srt 8.6 kB
  • 7. Regression models other than OLS/3. Subset selection in R.srt 8.6 kB
  • 6. Linear Regression Model/12. Bias Variance trade-off.srt 8.4 kB
  • 15. Ensemble technique 1 - Bagging/2. Bagging in R.srt 8.4 kB
  • 14. Simple Classification Tree/1. Classification Trees.srt 8.3 kB
  • 3. Basics of Statistics/4. Measures of Centers.srt 8.3 kB
  • 12. Comparing results from 3 models/1. Understanding the results of classification models.srt 8.0 kB
  • 9. Logistic Regression/8. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt 7.8 kB
  • 15. Ensemble technique 1 - Bagging/1. Bagging.srt 7.8 kB
  • 2. Setting up R Studio and R crash course/9. Creating Histograms in R.srt 7.8 kB
  • 6. Linear Regression Model/6. Multiple Linear Regression.srt 7.6 kB
  • 2. Setting up R Studio and R crash course/1. Installing R and R studio.srt 7.5 kB
  • 21. Creating Support Vector Machine Model in R/6. Radial Kernel with Hyperparameter Tuning.srt 7.5 kB
  • 13. Simple Decision Trees/7. Splitting Data into Test and Train Set in R.srt 7.5 kB
  • 5. Data Preprocessing for Regression Analysis/18. Correlation Matrix in R.srt 7.4 kB
  • 21. Creating Support Vector Machine Model in R/4. Hyperparameter Tuning for Linear Kernel.srt 7.3 kB
  • 6. Linear Regression Model/8. Interpreting result for categorical Variable.srt 7.1 kB
  • 8. Introduction to the classification Models/1. Three classification models and Data set.srt 6.8 kB
  • 5. Data Preprocessing for Regression Analysis/16. Dummy variable creation in R.srt 6.6 kB
  • 5. Data Preprocessing for Regression Analysis/14. Non Usable Variables.srt 6.4 kB
  • 18. Support Vector Machines/2. The Concept of a Hyperplane.srt 6.4 kB
  • 12. Comparing results from 3 models/2. Summary of the three models.srt 6.3 kB
  • 9. Logistic Regression/3. Results of Simple Logistic Regression.srt 6.2 kB
  • 8. Introduction to the classification Models/4. Why can't we use Linear Regression.srt 5.8 kB
  • 2. Setting up R Studio and R crash course/5. Inputting data part 1 Inbuilt datasets of R.srt 5.7 kB
  • 16. Ensemble technique 2 - Random Forest/2. Random Forest in R.srt 5.7 kB
  • 5. Data Preprocessing for Regression Analysis/15. Dummy variable creation Handling qualitative data.srt 5.7 kB
  • 13. Simple Decision Trees/9. Pruning a tree.srt 5.5 kB
  • 7. Regression models other than OLS/1. Linear models other than OLS.srt 5.4 kB
  • 3. Basics of Statistics/5. Measures of Dispersion.srt 5.4 kB
  • 3. Basics of Statistics/1. Types of Data.srt 5.3 kB
  • 9. Logistic Regression/6. Confusion Matrix.srt 5.3 kB
  • 16. Ensemble technique 2 - Random Forest/1. Random Forest technique.srt 5.2 kB
  • 5. Data Preprocessing for Regression Analysis/7. Outlier Treatment.srt 5.0 kB
  • 5. Data Preprocessing for Regression Analysis/8. Outlier Treatment in R.srt 5.0 kB
  • 13. Simple Decision Trees/1. Introduction to Decision trees.srt 4.7 kB
  • 18. Support Vector Machines/3. Maximum Margin Classifier.srt 4.5 kB
  • 9. Logistic Regression/2. Training a Simple Logistic model in R.srt 4.4 kB
  • 13. Simple Decision Trees/4. The stopping criteria for controlling tree growth.srt 4.4 kB
  • 5. Data Preprocessing for Regression Analysis/9. Missing Value imputation.srt 4.3 kB
  • 5. Data Preprocessing for Regression Analysis/11. Seasonality in Data.srt 4.2 kB
  • 5. Data Preprocessing for Regression Analysis/10. Missing Value imputation in R.srt 4.2 kB
  • 2. Setting up R Studio and R crash course/2. This is a milestone!.srt 4.0 kB
  • 5. Data Preprocessing for Regression Analysis/2. Data Exploration.srt 3.9 kB
  • 5. Data Preprocessing for Regression Analysis/1. Gathering Business Knowledge.srt 3.9 kB
  • 5. Data Preprocessing for Regression Analysis/5. Univariate Analysis and EDD.srt 3.8 kB
  • 2. Setting up R Studio and R crash course/6. Inputting data part 2 Manual data entry.srt 3.8 kB
  • 3. Basics of Statistics/2. Types of Statistics.srt 3.4 kB
  • 18. Support Vector Machines/1. Introduction to SVM.srt 3.2 kB
  • 18. Support Vector Machines/4. Limitations of Maximum Margin Classifier.srt 3.2 kB
  • 9. Logistic Regression/4. Logistic with multiple predictors.srt 3.1 kB
  • 1. Welcome to the course/1. Introduction.srt 3.0 kB
  • 5. Data Preprocessing for Regression Analysis/4. Importing the dataset into R.srt 2.9 kB
  • 21. Creating Support Vector Machine Model in R/2. Importing and preprocessing data.srt 2.8 kB
  • 14. Simple Classification Tree/2. The Data set for Classification problem.srt 2.4 kB
  • 22. Congratulations & about your certificate/2. Bonus Lecture.html 2.4 kB
  • 14. Simple Classification Tree/4. Advantages and Disadvantages of Decision Trees.srt 2.2 kB
  • 9. Logistic Regression/5. Training multiple predictor Logistic model in R.srt 2.1 kB
  • 19. Support Vector Classifier/2. Limitations of Support Vector Classifiers.srt 1.9 kB
  • 6. Linear Regression Model/1. The problem statement.srt 1.9 kB
  • 8. Introduction to the classification Models/3. The problem statements.srt 1.8 kB
  • 22. Congratulations & about your certificate/1. The final milestone!.srt 1.8 kB
  • 8. Introduction to the classification Models/2. Importing the data into R.srt 1.4 kB
  • 6. Linear Regression Model/13. More about test-train split.html 559 Bytes
  • 1. Welcome to the course/2. Course Resources.html 346 Bytes
  • 6. Linear Regression Model/15. Assignment 1 Regression Analysis.html 185 Bytes
  • 20. Support Vector Machines/2. Quiz.html 181 Bytes
  • 4. Intorduction to Machine Learning/3. Quiz Introduction to Machine Learning.html 181 Bytes
  • 5. Data Preprocessing for Regression Analysis/19. Quiz.html 181 Bytes
  • 6. Linear Regression Model/10. Quiz.html 181 Bytes
  • 9. Logistic Regression/9. Quiz.html 181 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 11. K-Nearest Neighbors/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 11. K-Nearest Neighbors/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 13. Simple Decision Trees/5. Course resources Notes and Datasets.html 79 Bytes
  • 21. Creating Support Vector Machine Model in R/1. Course resources Notes and Datasets.html 52 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 11. K-Nearest Neighbors/0. Websites you may like/[GigaCourse.Com].url 49 Bytes

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