Udemy | Beginner to Advanced Guide on Machine Learning with R Tool [FTU] GloDLS
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Torrent File Contents
Udemy | Beginner to Advanced Guide on Machine Learning with R Tool [FTU] GloDLS
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0. Websites you may like/How you can help Team-FTU.txt
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.vtt
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.vtt
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp4
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.vtt
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.mp4
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.vtt
2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp4
2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.vtt
2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp4
2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.vtt
2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.vtt
3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4
3. Module-3 Classification/1. 3.1 Introduction to Classification.vtt
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.vtt
3. Module-3 Classification/3.1 Programs.zip.zip
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.vtt
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.vtt
3. Module-3 Classification/5.1 Programs.zip.zip
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.vtt
3. Module-3 Classification/7.1 Programs.zip.zip
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.mp4
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.vtt
4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp4
4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.vtt
4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.mp4
4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.vtt
4. Module-4 Black Box Method-Neural network and SVM/3.1 Programs.zip.zip
4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp4
4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.vtt
4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp4
4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.vtt
4. Module-4 Black Box Method-Neural network and SVM/5.1 Programs.zip.zip
4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp4
4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.vtt
4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp4
4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.vtt
4. Module-4 Black Box Method-Neural network and SVM/7.1 Programs.zip.zip
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp4
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.vtt
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.mp4
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.vtt
5. Module-5 Tree Based Models/2.1 Programs.zip.zip
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp4
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.vtt
5. Module-5 Tree Based Models/3.1 Programs.zip.zip
5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4
5. Module-5 Tree Based Models/3. 5.3 Bagging.vtt
5. Module-5 Tree Based Models/4.1 Programs.zip.zip
5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4
5. Module-5 Tree Based Models/4. 5.4 Boosting.vtt
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.vtt
5. Module-5 Tree Based Models/6.1 Programs.zip.zip
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.vtt
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.vtt
6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4
6. Module-6 Clustering/2. 6.2 K-Means Clustering.vtt
6. Module-6 Clustering/3.1 Programs.zip.zip
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.vtt
6. Module-6 Clustering/4.1 Programs.zip.zip
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.vtt
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.vtt
7. Module-7 Regression/2.1 Programs.zip.zip
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.vtt
7. Module-7 Regression/3.1 Programs.zip.zip
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.vtt
7. Module-7 Regression/4. 7.4 Logistic Regression.mp4
7. Module-7 Regression/4. 7.4 Logistic Regression.vtt
7. Module-7 Regression/5.1 Programs.zip.zip
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.mp4
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.vtt
7. Module-7 Regression/6. 7.6 Forecasting.mp4
7. Module-7 Regression/6. 7.6 Forecasting.vtt
7. Module-7 Regression/7.1 Programs.zip.zip
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.vtt
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0. Websites you may like/How you can help Team-FTU.txt
- 237 Bytes
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4
- 17.68 MB
1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.vtt
- 2.51 KB
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4
- 3.51 MB
1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.vtt
- 776 Bytes
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
- 6.06 MB
1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.vtt
- 4.15 KB
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/1. 2.1 Introduction to Cross Validation.vtt
- 2.36 KB
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/2. 2.2 Cross Validation Method.vtt
- 3.58 KB
2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip
- 10.96 KB
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4
- 15.76 MB
2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.vtt
- 8.21 KB
3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4
- 3.21 MB
3. Module-3 Classification/1. 3.1 Introduction to Classification.vtt
- 1.85 KB
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4
- 6.08 MB
3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.vtt
- 3.64 KB
3. Module-3 Classification/3.1 Programs.zip.zip
- 10.96 KB
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4
- 14.67 MB
3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.vtt
- 6.58 KB
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4
- 5.01 MB
3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.vtt
- 3.03 KB
3. Module-3 Classification/5.1 Programs.zip.zip
- 10.96 KB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4
- 34.04 MB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt
- 14.8 KB
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4
- 2.36 MB
3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.vtt
- 1.24 KB
3. Module-3 Classification/7.1 Programs.zip.zip
- 10.96 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
- 2.91 KB
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/1. 4.1 Introduction to Artificial Neural Network.vtt
- 1.62 KB
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/2. 4.2 Conceptualizing of Neural Network.vtt
- 2.47 KB
4. Module-4 Black Box Method-Neural network and SVM/3.1 Programs.zip.zip
- 10.96 KB
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/3. 4.3 Implement Neural Network in R.vtt
- 4.94 KB
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/4. 4.4 Back Propagation.vtt
- 1.64 KB
4. Module-4 Black Box Method-Neural network and SVM/5.1 Programs.zip.zip
- 10.96 KB
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/5. 4.5 Implementation of Back Propagation Network.vtt
- 1.52 KB
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/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
- 10.96 KB
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp4
- 8.84 MB
4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.vtt
- 3.81 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
- 10.96 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
- 3.67 KB
5. Module-5 Tree Based Models/3.1 Programs.zip.zip
- 10.96 KB
5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4
- 7.74 MB
5. Module-5 Tree Based Models/3. 5.3 Bagging.vtt
- 3.57 KB
5. Module-5 Tree Based Models/4.1 Programs.zip.zip
- 10.96 KB
5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4
- 10.8 MB
5. Module-5 Tree Based Models/4. 5.4 Boosting.vtt
- 5.95 KB
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4
- 4.09 MB
5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.vtt
- 2.38 KB
5. Module-5 Tree Based Models/6.1 Programs.zip.zip
- 10.96 KB
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4
- 7.43 MB
5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.vtt
- 3.37 KB
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4
- 2.88 MB
6. Module-6 Clustering/1. 6.1 Introduction to Clustering.vtt
- 1.79 KB
6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4
- 11.28 MB
6. Module-6 Clustering/2. 6.2 K-Means Clustering.vtt
- 7.64 KB
6. Module-6 Clustering/3.1 Programs.zip.zip
- 10.96 KB
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4
- 8.15 MB
6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.vtt
- 3.37 KB
6. Module-6 Clustering/4.1 Programs.zip.zip
- 10.96 KB
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4
- 7.15 MB
6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.vtt
- 3.45 KB
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4
- 4.57 MB
7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.vtt
- 2.58 KB
7. Module-7 Regression/2.1 Programs.zip.zip
- 10.96 KB
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4
- 12.31 MB
7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.vtt
- 5.85 KB
7. Module-7 Regression/3.1 Programs.zip.zip
- 10.96 KB
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4
- 10.26 MB
7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.vtt
- 5.21 KB
7. Module-7 Regression/4. 7.4 Logistic Regression.mp4
- 4.66 MB
7. Module-7 Regression/4. 7.4 Logistic Regression.vtt
- 2.66 KB
7. Module-7 Regression/5.1 Programs.zip.zip
- 10.96 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
- 3.14 KB
7. Module-7 Regression/6. 7.6 Forecasting.mp4
- 19.85 MB
7. Module-7 Regression/6. 7.6 Forecasting.vtt
- 2.9 KB
7. Module-7 Regression/7.1 Programs.zip.zip
- 10.96 KB
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4
- 38.13 MB
7. Module-7 Regression/7. 7.7 Implementation of Forecasting.vtt
- 2.65 KB
Torrent Description
Description:
Learn Machine Learning with the help of R programming
Created by: Elementary Learners
Last updated: 2/2019
Language: English
Caption (CC): Included
Torrent Contains: 99 Files, 8 Folders
Course Source: https://www.udemy.com/beginner-to-advanced-guide-on-machine-learning-with-r-tool/
What you'll learn
• Master Machine Learning
• Regression modelling
• knn algorithm
• naive bayes algorithm
• BPN(Back Propagation Network)
• SVM(Support Vector Machine)
• Decision Tree
• Forecasting
Requirements
• R programming
• R studio should be installed already
• Basic knowledge of programming
• Basic knowledge of mathematics
Description
Inspired by the field of Machine Learning? Then this course is for you!
This course is intended for both freshers and experienced hoping to make the bounce to Data Science.
R is a statistical programming language which provides tools to analyze data and for creating high-level graphics.
The topic of Machine Learning is getting exceptionally hot these days in light of the fact that these learning algorithms can be utilized as a part of a few fields from software engineering to venture managing an account. Students, at the end of this course, will be technically sound in the basics and the advanced concepts of Machine Learning.
Who this course is for:
• Freshers
• Professionals
• Anyone interested in machine learning.
Learn Machine Learning with the help of R programming
Created by: Elementary Learners
Last updated: 2/2019
Language: English
Caption (CC): Included
Torrent Contains: 99 Files, 8 Folders
Course Source: https://www.udemy.com/beginner-to-advanced-guide-on-machine-learning-with-r-tool/
What you'll learn
• Master Machine Learning
• Regression modelling
• knn algorithm
• naive bayes algorithm
• BPN(Back Propagation Network)
• SVM(Support Vector Machine)
• Decision Tree
• Forecasting
Requirements
• R programming
• R studio should be installed already
• Basic knowledge of programming
• Basic knowledge of mathematics
Description
Inspired by the field of Machine Learning? Then this course is for you!
This course is intended for both freshers and experienced hoping to make the bounce to Data Science.
R is a statistical programming language which provides tools to analyze data and for creating high-level graphics.
The topic of Machine Learning is getting exceptionally hot these days in light of the fact that these learning algorithms can be utilized as a part of a few fields from software engineering to venture managing an account. Students, at the end of this course, will be technically sound in the basics and the advanced concepts of Machine Learning.
Who this course is for:
• Freshers
• Professionals
• Anyone interested in machine learning.