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种子名称：
[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]
文件类型：
视频
文件数目：
38个文件
文件大小：
338.28 MB
收录时间：
2021-5-7 15:42
已经下载：
3次
资源热度：
313
最近下载：
2024-12-22 11:47
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magnet:?xt=urn:btih:08fa1cc0fce7c5b246c1a62023a81991e9d164e5&dn=[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 R Tool [FTU].torrent
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp417.68MB
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp43.51MB
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp43.7MB
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.mp46.06MB
2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp43.45MB
2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp45.33MB
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp415.76MB
3. Module-3 Classification/1. 3.1 Introduction to Classification.mp43.21MB
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp46.08MB
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp414.67MB
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp45.01MB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp434.04MB
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp42.36MB
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.mp46.4MB
4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp43.16MB
4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.mp45.32MB
4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp412.31MB
4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp42.64MB
4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp44.29MB
4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp44.94MB
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp48.84MB
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.mp44.9MB
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp48.7MB
5. Module-5 Tree Based Models/3. 5.3 Bagging.mp47.74MB
5. Module-5 Tree Based Models/4. 5.4 Boosting.mp410.8MB
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp44.09MB
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp47.43MB
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp42.88MB
6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp411.28MB
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp48.15MB
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp47.15MB
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp44.57MB
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp412.31MB
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp410.26MB
7. Module-7 Regression/4. 7.4 Logistic Regression.mp44.66MB
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.mp46.6MB
7. Module-7 Regression/6. 7.6 Forecasting.mp419.85MB
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp438.13MB
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