[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]

Creation Time	Aug. 20, 2023, 9:29 a.m.
Last Access Time	May 3, 2026, 3:26 p.m.
File Size	338.6 MB
Keywords	on FTU Beginner with FTUForum UDEMY Machine to R Learning Tool com Guide Advanced
Total Requests	132
Total Files	99
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7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4 38.1 MB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4 34.0 MB
7. Module-7 Regression/6. 7.6 Forecasting.mp4 19.9 MB
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4 17.7 MB
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4 15.8 MB
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4 14.7 MB
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4 12.3 MB
4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp4 12.3 MB
6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4 11.3 MB
5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4 10.8 MB
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4 10.3 MB
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp4 8.8 MB
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp4 8.7 MB
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4 8.2 MB
5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4 7.7 MB
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4 7.4 MB
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4 7.1 MB
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.mp4 6.6 MB
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.mp4 6.4 MB
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4 6.1 MB
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.mp4 6.1 MB
2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp4 5.3 MB
4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.mp4 5.3 MB
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4 5.0 MB
4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp4 4.9 MB
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.mp4 4.9 MB
7. Module-7 Regression/4. 7.4 Logistic Regression.mp4 4.7 MB
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4 4.6 MB
4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp4 4.3 MB
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4 4.1 MB
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp4 3.7 MB
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4 3.5 MB
2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp4 3.5 MB
3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4 3.2 MB
4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp4 3.2 MB
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4 2.9 MB
4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp4 2.6 MB
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4 2.4 MB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt 14.8 KB
2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip 11.0 KB
3. Module-3 Classification/3.1 Programs.zip.zip 11.0 KB
3. Module-3 Classification/5.1 Programs.zip.zip 11.0 KB
3. Module-3 Classification/7.1 Programs.zip.zip 11.0 KB
4. Module-4 Black Box Method-Neural network and SVM/3.1 Programs.zip.zip 11.0 KB
4. Module-4 Black Box Method-Neural network and SVM/5.1 Programs.zip.zip 11.0 KB
4. Module-4 Black Box Method-Neural network and SVM/7.1 Programs.zip.zip 11.0 KB
5. Module-5 Tree Based Models/2.1 Programs.zip.zip 11.0 KB
5. Module-5 Tree Based Models/3.1 Programs.zip.zip 11.0 KB
5. Module-5 Tree Based Models/4.1 Programs.zip.zip 11.0 KB
5. Module-5 Tree Based Models/6.1 Programs.zip.zip 11.0 KB
6. Module-6 Clustering/3.1 Programs.zip.zip 11.0 KB
6. Module-6 Clustering/4.1 Programs.zip.zip 11.0 KB
7. Module-7 Regression/2.1 Programs.zip.zip 11.0 KB
7. Module-7 Regression/3.1 Programs.zip.zip 11.0 KB
7. Module-7 Regression/5.1 Programs.zip.zip 11.0 KB
7. Module-7 Regression/7.1 Programs.zip.zip 11.0 KB
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.vtt 8.2 KB
6. Module-6 Clustering/2. 6.2 K-Means Clustering.vtt 7.6 KB
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.vtt 6.6 KB
5. Module-5 Tree Based Models/4. 5.4 Boosting.vtt 6.0 KB
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.vtt 5.9 KB
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.vtt 5.2 KB
4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.vtt 4.9 KB
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.vtt 4.2 KB
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.vtt 3.8 KB
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.vtt 3.7 KB
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.vtt 3.6 KB
2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.vtt 3.6 KB
5. Module-5 Tree Based Models/3. 5.3 Bagging.vtt 3.6 KB
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.vtt 3.4 KB
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.vtt 3.4 KB
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.vtt 3.4 KB
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.vtt 3.1 KB
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.vtt 3.0 KB
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.vtt 2.9 KB
7. Module-7 Regression/6. 7.6 Forecasting.vtt 2.9 KB
4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.vtt 2.8 KB
7. Module-7 Regression/4. 7.4 Logistic Regression.vtt 2.7 KB
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.vtt 2.7 KB
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.vtt 2.6 KB
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.vtt 2.6 KB
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.vtt 2.5 KB
4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.vtt 2.5 KB
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.vtt 2.4 KB
2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.vtt 2.4 KB
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.vtt 1.9 KB
3. Module-3 Classification/1. 3.1 Introduction to Classification.vtt 1.9 KB
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.vtt 1.8 KB
4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.vtt 1.6 KB
4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.vtt 1.6 KB
4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.vtt 1.5 KB
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.vtt 1.2 KB
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.vtt 776 bytes
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[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]

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