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

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

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FTUForum com UDEMY Beginner to Advanced Guide on Machine Learning with Tool FTU

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328B

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237B

1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4

17.68MB

1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.vtt

2.51KB

1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4

3.51MB

1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.vtt

776B

1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp4

3.7MB

1. Module-1 Introduction to Course/3. 1.3 What you will Learn.vtt

1.9KB

1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.mp4

6.06MB

1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.vtt

4.15KB

2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp4

3.45MB

2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.vtt

2.36KB

2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp4

5.33MB

2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.vtt

3.58KB

2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip

10.96KB

2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4

15.76MB

2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.vtt

8.21KB

3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4

3.21MB

3. Module-3 Classification/1. 3.1 Introduction to Classification.vtt

1.85KB

3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4

6.08MB

3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.vtt

3.64KB

3. Module-3 Classification/3.1 Programs.zip.zip

10.96KB

3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4

14.67MB

3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.vtt

6.58KB

3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4

5.01MB

3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.vtt

3.03KB

3. Module-3 Classification/5.1 Programs.zip.zip

10.96KB

3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4

34.04MB

3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt

14.8KB

3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4

2.36MB

3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.vtt

1.24KB

3. Module-3 Classification/7.1 Programs.zip.zip

10.96KB

3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.mp4

6.4MB

3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.vtt

2.91KB

4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp4

3.16MB

4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.vtt

1.62KB

4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.mp4

5.32MB

4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.vtt

2.47KB

4. Module-4 Black Box Method-Neural network and SVM/3.1 Programs.zip.zip

10.96KB

4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp4

12.31MB

4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.vtt

4.94KB

4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp4

2.64MB

4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.vtt

1.64KB

4. Module-4 Black Box Method-Neural network and SVM/5.1 Programs.zip.zip

10.96KB

4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp4

4.29MB

4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.vtt

1.52KB

4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp4

4.94MB

4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.vtt

2.8KB

4. Module-4 Black Box Method-Neural network and SVM/7.1 Programs.zip.zip

10.96KB

4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp4

8.84MB

4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.vtt

3.81KB

5. Module-5 Tree Based Models/1. 5.1 Decision Tree.mp4

4.9MB

5. Module-5 Tree Based Models/1. 5.1 Decision Tree.vtt

2.6KB

5. Module-5 Tree Based Models/2.1 Programs.zip.zip

10.96KB

5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp4

8.7MB

5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.vtt

3.67KB

5. Module-5 Tree Based Models/3.1 Programs.zip.zip

10.96KB

5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4

7.74MB

5. Module-5 Tree Based Models/3. 5.3 Bagging.vtt

3.57KB

5. Module-5 Tree Based Models/4.1 Programs.zip.zip

10.96KB

5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4

10.8MB

5. Module-5 Tree Based Models/4. 5.4 Boosting.vtt

5.95KB

5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4

4.09MB

5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.vtt

2.38KB

5. Module-5 Tree Based Models/6.1 Programs.zip.zip

10.96KB

5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4

7.43MB

5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.vtt

3.37KB

6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4

2.88MB

6. Module-6 Clustering/1. 6.1 Introduction to Clustering.vtt

1.79KB

6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4

11.28MB

6. Module-6 Clustering/2. 6.2 K-Means Clustering.vtt

7.64KB

6. Module-6 Clustering/3.1 Programs.zip.zip

10.96KB

6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4

8.15MB

6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.vtt

3.37KB

6. Module-6 Clustering/4.1 Programs.zip.zip

10.96KB

6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4

7.15MB

6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.vtt

3.45KB

7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4

4.57MB

7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.vtt

2.58KB

7. Module-7 Regression/2.1 Programs.zip.zip

10.96KB

7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4

12.31MB

7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.vtt

5.85KB

7. Module-7 Regression/3.1 Programs.zip.zip

10.96KB

7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4

10.26MB

7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.vtt

5.21KB

7. Module-7 Regression/4. 7.4 Logistic Regression.mp4

4.66MB

7. Module-7 Regression/4. 7.4 Logistic Regression.vtt

2.66KB

7. Module-7 Regression/5.1 Programs.zip.zip

10.96KB

7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.mp4

6.6MB

7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.vtt

3.14KB

7. Module-7 Regression/6. 7.6 Forecasting.mp4

19.85MB

7. Module-7 Regression/6. 7.6 Forecasting.vtt

2.9KB

7. Module-7 Regression/7.1 Programs.zip.zip

10.96KB

7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4

38.13MB

7. Module-7 Regression/7. 7.7 Implementation of Forecasting.vtt

2.65KB

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