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[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]
magnet:?xt=urn:btih:08fa1cc0fce7c5b246c1a62023a81991e9d164e5&dn=[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]
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Hash值:
08fa1cc0fce7c5b246c1a62023a81991e9d164e5
点击数:
261
文件大小:
338.28 MB
文件数量:
38
创建日期:
2021-5-7 15:42
最后访问:
2024-6-13 01:46
访问标签:
FTUForum
com
UDEMY
Beginner
to
Advanced
Guide
on
Machine
Learning
with
R
Tool
FTU
文件列表详情
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4 17.68 MB
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4 3.51 MB
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp4 3.7 MB
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.mp4 6.06 MB
2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp4 3.45 MB
2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp4 5.33 MB
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4 15.76 MB
3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4 3.21 MB
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4 6.08 MB
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4 14.67 MB
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4 5.01 MB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4 34.04 MB
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4 2.36 MB
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.mp4 6.4 MB
4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp4 3.16 MB
4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.mp4 5.32 MB
4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp4 12.31 MB
4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp4 2.64 MB
4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp4 4.29 MB
4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp4 4.94 MB
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp4 8.84 MB
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.mp4 4.9 MB
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp4 8.7 MB
5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4 7.74 MB
5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4 10.8 MB
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4 4.09 MB
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4 7.43 MB
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4 2.88 MB
6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4 11.28 MB
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4 8.15 MB
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4 7.15 MB
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4 4.57 MB
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4 12.31 MB
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4 10.26 MB
7. Module-7 Regression/4. 7.4 Logistic Regression.mp4 4.66 MB
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.mp4 6.6 MB
7. Module-7 Regression/6. 7.6 Forecasting.mp4 19.85 MB
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4 38.13 MB
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