Don't like banners? Log in and we will make sure you see no banners. For free.
[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]

[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]

Other
Size
339 MB
Seeders
0
Leechers
2
Files
94
Category
Other
Added
at 3:52pm GMT+1
Infohash
08fa1cc0fce7c5b246c1a62023a81991e9d164e5
Direct linkTorrent fileDownload now
Don't like banners? Log in and we will make sure you see no banners. For free.

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.

For More Udemy Free Courses >>> https://ftuforum.com/
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Our Forum for discussion >>> https://discuss.ftuforum.com/




File list
  • [FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]
  • 0. Websites you may like/How you can help Team-FTU.txt 237 B
  • 1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4 17.7 MB
  • 1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.vtt 2.5 KB
  • 1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4 3.5 MB
  • 1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.vtt 776 B
  • 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 6.1 MB
  • 1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.vtt 4.2 KB
  • 2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp4 3.5 MB
  • 2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.vtt 2.4 KB
  • 2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp4 5.3 MB
  • 2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.vtt 3.6 KB
  • 2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4 15.8 MB
  • 2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.vtt 8.2 KB
  • 2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip 11 KB
  • 3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4 3.2 MB
  • 3. Module-3 Classification/1. 3.1 Introduction to Classification.vtt 1.9 KB
  • 3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4 6.1 MB
  • 3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.vtt 3.6 KB
  • 3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4 14.7 MB
  • 3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.vtt 6.6 KB
  • 3. Module-3 Classification/3.1 Programs.zip.zip 11 KB
  • 3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4 5 MB
  • 3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.vtt 3 KB
  • 3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4 34 MB
  • 3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt 14.8 KB
  • 3. Module-3 Classification/5.1 Programs.zip.zip 11 KB
  • 3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4 2.4 MB
  • 3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.vtt 1.2 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 2.9 KB
  • 3. Module-3 Classification/7.1 Programs.zip.zip 11 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp4 3.2 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.vtt 1.6 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.mp4 5.3 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.vtt 2.5 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp4 12.3 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.vtt 4.9 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/3.1 Programs.zip.zip 11 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp4 2.6 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.vtt 1.6 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp4 4.3 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.vtt 1.5 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/5.1 Programs.zip.zip 11 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp4 4.9 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. 4.7 Implementation of SVM in R.mp4 8.8 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.vtt 3.8 KB
  • 4. Module-4 Black Box Method-Neural network and SVM/7.1 Programs.zip.zip 11 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. 5.2 Implementation of Decision Tree.mp4 8.7 MB
  • 5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.vtt 3.7 KB
  • 5. Module-5 Tree Based Models/2.1 Programs.zip.zip 11 KB
  • 5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4 7.7 MB
  • 5. Module-5 Tree Based Models/3. 5.3 Bagging.vtt 3.6 KB
  • 5. Module-5 Tree Based Models/3.1 Programs.zip.zip 11 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 6 KB
  • 5. Module-5 Tree Based Models/4.1 Programs.zip.zip 11 KB
  • 5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4 4.1 MB
  • 5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.vtt 2.4 KB
  • 5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4 7.4 MB
  • 5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.vtt 3.4 KB
  • 5. Module-5 Tree Based Models/6.1 Programs.zip.zip 11 KB
  • 6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4 2.9 MB
  • 6. Module-6 Clustering/1. 6.1 Introduction to Clustering.vtt 1.8 KB
  • 6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4 11.3 MB
  • 6. Module-6 Clustering/2. 6.2 K-Means Clustering.vtt 7.6 KB
  • 6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4 8.2 MB
  • 6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.vtt 3.4 KB
  • 6. Module-6 Clustering/3.1 Programs.zip.zip 11 KB
  • 6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4 7.1 MB
  • 6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.vtt 3.4 KB
  • 6. Module-6 Clustering/4.1 Programs.zip.zip 11 KB
  • 7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4 4.6 MB
  • 7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.vtt 2.6 KB
  • 7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4 12.3 MB
  • 7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.vtt 5.9 KB
  • 7. Module-7 Regression/2.1 Programs.zip.zip 11 KB
  • 7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4 10.3 MB
  • 7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.vtt 5.2 KB
  • 7. Module-7 Regression/3.1 Programs.zip.zip 11 KB
  • 7. Module-7 Regression/4. 7.4 Logistic Regression.mp4 4.7 MB
  • 7. Module-7 Regression/4. 7.4 Logistic Regression.vtt 2.7 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 3.1 KB
  • 7. Module-7 Regression/5.1 Programs.zip.zip 11 KB
  • 7. Module-7 Regression/6. 7.6 Forecasting.mp4 19.9 MB
  • 7. Module-7 Regression/6. 7.6 Forecasting.vtt 2.9 KB
  • 7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4 38.1 MB
  • 7. Module-7 Regression/7. 7.7 Implementation of Forecasting.vtt 2.7 KB
  • 7. Module-7 Regression/7.1 Programs.zip.zip 11 KB

Rating
Not rated yet
Log in to rate

Comments

No comments yet.


Similar torrents
NameSizeDate
1.9 GB01
388 MB01
263 MB00
3 GB01
8 MB00