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Udemy | Beginner to Advanced Guide on Machine Learning with R Tool [FTU] TGx Exclusive

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Category:

Other > Training

Language:

English

Total Size:

338.59 MB

Info Hash:

08fa1cc0fce7c5b246c1a62023a81991e9d164e5

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Prom3th3uS :_verified_uploader:

Added:

30-06-2019 14:42

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668

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109

File	Size
0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url	328.00 B
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0. Websites you may like/3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url	163.00 B
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0. Websites you may like/5. (Discuss.FTUForum.com) FTU Discussion Forum.url	294.00 B
0. Websites you may like/How you can help Team-FTU.txt	237.00 B
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	776.00 B
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp4	3.70 MB
1. Module-1 Introduction to Course/3. 1.3 What you will Learn.vtt	1.90 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. 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
2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip	10.96 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. 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/3.1 Programs.zip.zip	10.96 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. 3.5 Implementation of Naive-Bayes Classifier.mp4	34.04 MB
3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt	14.80 kB
3. Module-3 Classification/5.1 Programs.zip.zip	10.96 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. 3.7 Implementation of Linear Discriminant Analysis.mp4	6.40 MB
3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.vtt	2.91 kB
3. Module-3 Classification/7.1 Programs.zip.zip	10.96 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. 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/3.1 Programs.zip.zip	10.96 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. 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/5.1 Programs.zip.zip	10.96 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	2.80 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
4. Module-4 Black Box Method-Neural network and SVM/7.1 Programs.zip.zip	10.96 kB
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.mp4	4.90 MB
5. Module-5 Tree Based Models/1. 5.1 Decision Tree.vtt	2.60 kB
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp4	8.70 MB
5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.vtt	3.67 kB
5. Module-5 Tree Based Models/2.1 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/3.1 Programs.zip.zip	10.96 kB
5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4	10.80 MB
5. Module-5 Tree Based Models/4. 5.4 Boosting.vtt	5.95 kB
5. Module-5 Tree Based Models/4.1 Programs.zip.zip	10.96 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. 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
5. Module-5 Tree Based Models/6.1 Programs.zip.zip	10.96 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. 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/3.1 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
6. Module-6 Clustering/4.1 Programs.zip.zip	10.96 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. 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/2.1 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/3.1 Programs.zip.zip	10.96 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. 7.5 Implementation of Logistic Regression.mp4	6.60 MB
7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.vtt	3.14 kB
7. Module-7 Regression/5.1 Programs.zip.zip	10.96 kB
7. Module-7 Regression/6. 7.6 Forecasting.mp4	19.85 MB
7. Module-7 Regression/6. 7.6 Forecasting.vtt	2.90 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
7. Module-7 Regression/7.1 Programs.zip.zip	10.96 kB

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.

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