[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2021-5-7 15:42 | 2024-5-9 10:02 | 257 | 338.28 MB | 38 |
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文件列表
- 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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