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

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1. Module-1 Introduction to Course
  • 1. 1.1 Introduction to the Course.mp4 (17.7 MB)
  • 1. 1.1 Introduction to the Course.vtt (2.5 KB)
  • 2. 1.2 Pre-Requisite.mp4 (3.5 MB)
  • 2. 1.2 Pre-Requisite.vtt (0.8 KB)
  • 3. 1.3 What you will Learn.mp4 (3.7 MB)
  • 3. 1.3 What you will Learn.vtt (1.9 KB)
  • 4. 1.4 Techniques of Machine Learning.mp4 (6.1 MB)
  • 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)
  • 1. 2.1 Introduction to Cross Validation.vtt (2.4 KB)
  • 2. 2.2 Cross Validation Method.mp4 (5.3 MB)
  • 2. 2.2 Cross Validation Method.vtt (3.6 KB)
  • 3.1 Programs.zip.zip (11.0 KB)
  • 3. 2.3 Caret package.mp4 (15.8 MB)
  • 3. 2.3 Caret package.vtt (8.2 KB)
3. Module-3 Classification
  • 1. 3.1 Introduction to Classification.mp4 (3.2 MB)
  • 1. 3.1 Introduction to Classification.vtt (1.9 KB)
  • 2. 3.2 KNN- K Nearest Neighbors.mp4 (6.1 MB)
  • 2. 3.2 KNN- K Nearest Neighbors.vtt (3.6 KB)
  • 3.1 Programs.zip.zip (11.0 KB)
  • 3. 3.3 Implementation of KNN Algorithm.mp4 (14.7 MB)
  • 3. 3.3 Implementation of KNN Algorithm.vtt (6.6 KB)
  • 4. 3.4 Naive-Bayes Classifier.mp4 (5.0 MB)
  • 4. 3.4 Naive-Bayes Classifier.vtt (3.0 KB)
  • 5.1 Programs.zip.zip (11.0 KB)
  • 5. 3.5 Implementation of Naive-Bayes Classifier.mp4 (34.0 MB)
  • 5. 3.5 Implementation of Naive-Bayes Classifier.vtt (14.8 KB)
  • 6. 3.6 Linear Discriminant Analysis.mp4 (2.4 MB)
  • 6. 3.6 Linear Discriminant Analysis.vtt (1.2 KB)
  • 7.1 Programs.zip.zip (11.0 KB)
  • 7. 3.7 Implementation of Linear Discriminant Analysis.mp4 (6.4 MB)
  • 7. 3.7 Implementation of Linear Discriminant Analysis.vtt (2.9 KB)
4. Module-4 Black Box Method-Neural network and SVM
  • 1. 4.1 Introduction to Artificial Neural Network.mp4 (3.2 MB)
  • 1. 4.1 Introduction to Artificial Neural Network.vtt (1.6 KB)
  • 2. 4.2 Conceptualizing of Neural Network.mp4 (5.3 MB)
  • 2. 4.2 Conceptualizing of Neural Network.vtt (2.5 KB)
  • 3.1 Programs.zip.zip (11.0 KB)
  • 3. 4.3 Implement Neural Network in R.mp4 (12.3 MB)
  • 3. 4.3 Implement Neural Network in R.vtt (4.9 KB)
  • 4. 4.4 Back Propagation.mp4 (2.6 MB)
  • 4. 4.4 Back Propagation.vtt (1.6 KB)
  • 5.1 Programs.zip.zip (11.0 KB)
  • 5. 4.5 Implementation of Back Propagation Network.mp4 (4.3 MB)
  • 5. 4.5 Implementation of Back Propagation Network.vtt (1.5 KB)
  • 6. 4.6 Introduction to Support Vector Machine.mp4 (4.9 MB)
  • 6. 4.6 Introduction to Support Vector Machine.vtt (2.8 KB)
  • 7.1 Programs.zip.zip (11.0 KB)
  • 7. 4.7 Implementation of SVM in R.mp4 (8.8 MB)
  • 7. 4.7 Implementation of SVM in R.vtt (3.8 KB)
5. Module-5 Tree Based Models
  • 1. 5.1 Decision Tree.mp4 (4.9 MB)
  • 1. 5.1 Decision Tree.vtt (2.6 KB)
  • 2.1 Programs.zip.zip (11.0 KB)
  • 2. 5.2 Implementation of Decision Tree.mp4 (8.7 MB)
  • 2. 5.2 Implementation of Decision Tree.vtt (3.7 KB)
  • 3.1 Programs.zip.zip (11.0 KB)
  • 3. 5.3 Bagging.mp4 (7.7 MB)
  • 3. 5.3 Bagging.vtt (3.6 KB)
  • 4.1 Programs.zip.zip (11.0 KB)
  • 4. 5.4 Boosting.mp4 (10.8 MB)
  • 4. 5.4 Boosting.vtt (6.0 KB)
  • 5. 5.5 Introduction to Random Forest.mp4 (4.1 MB)
  • 5. 5.5 Introduction to Random Forest.vtt (2.4 KB)
  • 6.1 Programs.zip.zip (11.0 KB)
  • 6. 5.6 Implementation of Random Forest.mp4 (7.4 MB)
  • 6. 5.6 Implementation of Random Forest.vtt (3.4 KB)
6. Module-6 Clustering
  • 1. 6.1 Introduction to Clustering.mp4 (2.9 MB)
  • 1. 6.1 Introduction to Clustering.vtt (1.8 KB)
  • 2. 6.2 K-Means Clustering.mp4 (11.3 MB)
  • 2. 6.2 K-Means Clustering.vtt (7.6 KB)
  • 3.1 Programs.zip.zip (11.0 KB)
  • 3. 6.3 Implementation of K-Means Clustering.mp4 (8.2 MB)
  • 3. 6.3 Implementation of K-Means Clustering.vtt (3.4 KB)
  • 4.1 Programs.zip.zip (11.0 KB)
  • 4. 6.4 Hierarchical Clustering.mp4 (7.1 MB)
  • 4. 6.4 Hierarchical Clustering.vtt (3.4 KB)
7. Module-7 Regression
  • 1. 7.1 Predicting with Linear Regression.mp4 (4.6 MB)
  • 1. 7.1 Predicting with Linear Regression.vtt (2.6 KB)
  • 2.1 Programs.zip.zip (11.0 KB)
  • 2. 7.2 Implementation of Linear Regression.mp4 (12.3 MB)
  • 2. 7.2 Implementation of Linear Regression.vtt (5.9 KB)
  • 3.1 Programs.zip.zip (11.0 KB)
  • 3. 7.3 Multiple Covariates Regression.mp4 (10.3 MB)
  • 3. 7.3 Multiple Covariates Regression.vtt (5.2 KB)
  • 4. 7.4 Logistic Regression.mp4 (4.7 MB)
  • 4. 7.4 Logistic Regression.vtt (2.7 KB)
  • 5.1 Programs.zip.zip (11.0 KB)
  • 5. 7.5 Implementation of Logistic Regression.mp4 (6.6 MB)
  • 5. 7.5 Implementation of Logistic Regression.vtt (3.1 KB)
  • 6. 7.6 Forecasting.mp4 (19.9 MB)
  • 6. 7.6 Forecasting.vtt (2.9 KB)
  • 7.1 Programs.zip.zip (11.0 KB)
  • 7. 7.7 Implementation of Forecasting.mp4 (38.1 MB)
  • 7. 7.7 Implementation of Forecasting.vtt (2.7 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.

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/





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

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Download torrent
338.6 MB
seeders:18
leechers:5
Udemy | Beginner to Advanced Guide on Machine Learning with R Tool [FTU]

Torrent hash: 08FA1CC0FCE7C5B246C1A62023A81991E9D164E5