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

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328 B
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239 B
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294 B
0. Websites you may like/How you can help Team-FTU.txt
237 B
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4
18 MB
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.vtt
2.5 kB
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4
3.5 MB
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.vtt
776 B
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp4
3.7 MB
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.vtt
1.9 kB
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.mp4
6.1 MB
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.vtt
4.2 kB
2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp4
3.5 MB
2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.vtt
2.4 kB
2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp4
5.3 MB
2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.vtt
3.6 kB
2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip
11 kB
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4
16 MB
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.vtt
8.2 kB
3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4
3.2 MB
3. Module-3 Classification/1. 3.1 Introduction to Classification.vtt
1.9 kB
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4
6.1 MB
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.vtt
3.6 kB
3. Module-3 Classification/3.1 Programs.zip.zip
11 kB
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4
15 MB
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.vtt
6.6 kB
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4
5.0 MB
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.vtt
3.0 kB
3. Module-3 Classification/5.1 Programs.zip.zip
11 kB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4
34 MB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt
15 kB
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4
2.4 MB
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.vtt
1.2 kB
3. Module-3 Classification/7.1 Programs.zip.zip
11 kB
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.mp4
6.4 MB
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.vtt
2.9 kB
4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp4
3.2 MB
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/2. 4.2 Conceptualizing of Neural Network.mp4
5.3 MB
4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.vtt
2.5 kB
4. Module-4 Black Box Method-Neural network and SVM/3.1 Programs.zip.zip
11 kB
4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp4
12 MB
4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.vtt
4.9 kB
4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp4
2.6 MB
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/5.1 Programs.zip.zip
11 kB
4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp4
4.3 MB
4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.vtt
1.5 kB
4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp4
4.9 MB
4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.vtt
2.8 kB
4. Module-4 Black Box Method-Neural network and SVM/7.1 Programs.zip.zip
11 kB
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp4
8.8 MB
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/1. 5.1 Decision Tree.mp4
4.9 MB
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.vtt
2.6 kB
5. Module-5 Tree Based Models/2.1 Programs.zip.zip
11 kB
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp4
8.7 MB
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.vtt
3.7 kB
5. Module-5 Tree Based Models/3.1 Programs.zip.zip
11 kB
5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4
7.7 MB
5. Module-5 Tree Based Models/3. 5.3 Bagging.vtt
3.6 kB
5. Module-5 Tree Based Models/4.1 Programs.zip.zip
11 kB
5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4
11 MB
5. Module-5 Tree Based Models/4. 5.4 Boosting.vtt
6.0 kB
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4
4.1 MB
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.vtt
2.4 kB
5. Module-5 Tree Based Models/6.1 Programs.zip.zip
11 kB
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4
7.4 MB
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.vtt
3.4 kB
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4
2.9 MB
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.vtt
1.8 kB
6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4
11 MB
6. Module-6 Clustering/2. 6.2 K-Means Clustering.vtt
7.6 kB
6. Module-6 Clustering/3.1 Programs.zip.zip
11 kB
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4
8.2 MB
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.vtt
3.4 kB
6. Module-6 Clustering/4.1 Programs.zip.zip
11 kB
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4
7.1 MB
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.vtt
3.4 kB
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4
4.6 MB
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.vtt
2.6 kB
7. Module-7 Regression/2.1 Programs.zip.zip
11 kB
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4
12 MB
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.vtt
5.9 kB
7. Module-7 Regression/3.1 Programs.zip.zip
11 kB
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4
10 MB
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.vtt
5.2 kB
7. Module-7 Regression/4. 7.4 Logistic Regression.mp4
4.7 MB
7. Module-7 Regression/4. 7.4 Logistic Regression.vtt
2.7 kB
7. Module-7 Regression/5.1 Programs.zip.zip
11 kB
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.mp4
6.6 MB
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.vtt
3.1 kB
7. Module-7 Regression/6. 7.6 Forecasting.mp4
20 MB
7. Module-7 Regression/6. 7.6 Forecasting.vtt
2.9 kB
7. Module-7 Regression/7.1 Programs.zip.zip
11 kB
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4
38 MB
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.vtt
2.7 kB