Certificate Program in Machine Learning using R Programming

Learn concepts of advanced machine learning using R programming with hands-on case studies

paid 5(2 Ratings) 56 Students Enrolled
Created By Imurgence Learning Last Updated Thu, 18-Apr-2019
+ View More
Description
Course Description
This Imurgence course is comprised of the following topics: decision tree, ensemble learning, support vector machines, market basket analysis, k-nearest neighbours, clustering and artificial neural network.

  • Upon successful completion of this course, the learner will be skilled in R programming to perform data analytics on business data.

Target Audience
This course is ideal for anyone looking to improve their skills or start a career in data science, business analytics, artificial intelligence (AI) or machine learning.

Access Time frame
Six months from the date of access.

Prerequisites
As a prerequisites for this course a general understanding of statistics is required and experience in R Programming is required. It would be advisable to do the Certificate course in Data Analytics using R

Type of Certification
Certificate of Completion

Format of Certification
Digital

Professional Association/Affiliation
The certificate is issued by Imurgence an autonomous institution and endorsed by SiCureMi an IIT Delhi incubated Analytics Firm

Method of Obtaining Certification
Upon successfully completing 80% of this course, the learner will be able to download digital copy of the Certificate and Mark sheet from the Certificates section . The Mark Sheet will keep on updating as the learner progresses towards 100% completion.



Curriculum For This Course
119 Lessons 10:46:37 Hours
Decision Tree
13 Lessons 01:13:18 Hours
  • Introduction to Machine Learning 1.0 00:04:44
  • Decision Tree 1.1 00:05:59
  • Decision Tree 1.2 00:06:42
  • Decision Tree 1.3 00:03:09
  • Decision Tree 1.4 00:05:57
  • Decision Tree 1.5 00:04:55
  • Decision Tree 1.6 00:03:04
  • Decision Tree 1.7 00:06:39
  • Decision Tree 1.8 00:09:28
  • Decision Tree 1.9 00:09:18
  • Decision Tree on Diabetes Dataset 1.10 00:03:54
  • Decision Tree on Diabetes Dataset 1.11 00:06:59
  • Decision Tree on Diabetes Dataset 1.12 00:02:30
  • Random Forest 2.1 00:08:00
  • Random Forest 2.2 00:03:22
  • Random Forest 2.3 00:05:39
  • Random Forest 2.4 00:05:23
  • Random Forest 2.5 00:06:37
  • Random Forest on Diabetes Dataset 2.6 00:03:56
  • Random Forest on Diabetes Dataset 2.7 00:06:59
  • Random Forest on Diabetes Dataset 2.8 00:02:30
  • Random Forest on Diabetes Dataset 2.9 00:05:26
  • Random Forest Regression 2.10 00:04:27
  • Random Forest Regression 2.11 00:05:17
  • Random Forest Regression 2.12 00:01:01
  • Random Forest Regression 2.13 00:04:50
  • Random Forest Regression 2.14 00:05:32
  • Random Forest Regression 2.15 00:02:56
  • IT Network Intrusion Detection Case using Decision Tree 2.16 00:05:37
  • IT Network Intrusion Detection Case using Decision Tree 2.17 00:07:30
  • IT Network Intrusion Detection Case using Decision Tree 2.18 00:04:39
  • IT Network Intrusion Detection Case using Decision Tree 2.19 00:03:01
  • IT Network Intrusion Detection Case using Decision Tree 2.20 00:07:44
  • IT Network Intrusion Detection Case using Decision Tree 2.21 00:03:02
  • IT Network Intrusion Detection Case using Decision Tree 2.22 00:08:12
  • IT Network Intrusion Detection Case using Decision Tree 2.23 00:04:37
  • IT Network Intrusion Detection Case using Decision Tree 2.24 00:04:14
  • Support Vector Machine 3.1 00:04:00
  • Support Vector Machine 3.2 00:04:35
  • Support Vector Machine 3.3 00:04:44
  • Support Vector Machine 3.4 00:02:54
  • Support Vector Machine 3.5 00:08:42
  • Support Vector Machine 3.6 00:04:41
  • Support Vector Machine 3.7 00:04:20
  • Support Vector Machine 3.8 00:06:18
  • Support Vector Machine 3.9 00:05:35
  • Support Vector Machine 3.10 00:02:59
  • Support Vector Machine 3.11 00:03:11
  • Support Vector Machine 3.12 00:03:29
  • Support Vector Machine 3.13 00:03:03
  • SVM on Iris Dataset 3.14 00:03:45
  • SVM on Iris Dataset 3.15 00:08:13
  • SVM on Iris Dataset 3.16 00:07:08
  • SVM on Iris Dataset 3.17 00:07:12
  • SVM on Iris Dataset 3.18 00:04:30
  • SVM on Iris Dataset 3.19 00:04:07
  • Credit Risk Case Using SVM 3.20 00:04:02
  • Credit Risk Case Using SVM 3.21 00:04:08
  • Credit Risk Case Using SVM 3.22 00:05:13
  • Credit Risk Case Using SVM 3.23 00:06:50
  • Credit Risk Case Using SVM 3.24 00:07:25
  • K Fold Cross Validation 3.25 00:04:16
  • K Fold Cross Validation 3.26 00:09:09
  • K Fold Cross Validation 3.27 00:02:10
  • K Fold Cross Validation 3.28 00:07:28
  • Market Basket Analysis 4.1 00:06:48
  • Market Basket Analysis 4.2 00:06:12
  • Market Basket Analysis 4.3 00:03:06
  • Market Basket Analysis 4.4 00:02:35
  • French Store Analysis Using MBA 4.5 00:05:34
  • French Store Analysis Using MBA 4.6 00:05:10
  • French Store Analysis Using MBA 4.7 00:03:36
  • French Store Analysis Using MBA 4.8 00:08:37
  • French Store Analysis Using MBA 4.9 00:05:32
  • k Nearest Neighbours 5.1 00:07:31
  • k Nearest Neighbours 5.2 00:03:36
  • k Nearest Neighbours 5.3 00:05:09
  • kNN on Advertisement Dataset 5.4 00:04:50
  • kNN on Advertisement Dataset 5.5 00:05:19
  • kNN on Advertisement Dataset 5.6 00:04:16
  • kNN on Cancer Dataset 5.7 00:02:42
  • kNN on Cancer Dataset 5.8 00:06:17
  • kNN on Cancer Dataset 5.9 00:03:48
  • kNN on Cancer Dataset 5.10 00:04:43
  • kNN on Cancer Dataset 5.11 00:05:38
  • kNN on Cancer Dataset 5.12 00:09:03
  • Customer Churn using kNN 5.13 00:11:11
  • Customer Churn using kNN 5.14 00:06:50
  • Customer Churn using kNN 5.15 00:06:12
  • Customer Churn using kNN 5.16 00:10:32
  • kMeans 6.1 00:03:18
  • kMeans 6.2 00:05:45
  • kMeans 6.3 00:03:42
  • kMeans 6.4 00:04:24
  • kMeans 6.5 00:06:30
  • kMeans Customer Segmentation Case 6.6 00:03:35
  • kMeans Customer Segmentation Case 6.7 00:06:26
  • kMeans Customer Segmentation Case 6.8 00:04:15
  • kMeans Customer Segmentation Case 6.9 00:06:05
  • kMeans Customer Segmentation Case 6.10 00:03:30
  • Artificial Neural Network 7.1 00:01:52
  • Artificial Neural Network 7.2 00:07:35
  • Artificial Neural Network 7.3 00:06:14
  • Artificial Neural Network 7.4 00:02:04
  • Artificial Neural Network 7.5 00:03:59
  • Artificial Neural Network 7.6 00:06:41
  • Artificial Neural Network 7.7 00:06:42
  • Artificial Neural Network 7.8 00:03:32
  • Artificial Neural Network 7.9 00:08:28
  • Artificial Neural Network 7.10 00:15:31
  • Artificial Neural Network 7.11 00:11:24
  • Artificial Neural Network 7.12 00:08:17
  • Artificial Neural Network 7.13 00:05:57
  • Bank Customer Churn Case Using ANN 7.14 00:05:35
  • Bank Customer Churn Case Using ANN 7.15 00:04:20
  • Bank Customer Churn Case Using ANN 7.16 00:05:31
  • Bank Customer Churn Case Using ANN 7.17 00:05:38
  • Bank Customer Churn Case Using ANN 7.18 00:05:09
  • Bank Customer Churn Case Using ANN 7.19 00:01:55
+ View More
Other Related Courses
About The Instructor
  • 7 Reviews
  • 102 Students
  • 21 Courses
+ View More
A Data Science Training Company
Powered by Simple & Real Analytics
Student Feedback
5
Average Rating
  • 0%
  • 0%
  • 0%
  • 0%
  • 100%
Reviews
  • Tue, 12-Mar-2019
    Akash khanvilkar
    Had a good experience so far. Love your case studies.
  • Sun, 17-Mar-2019
    Rahul Palkar
    This course is revision of all basic concepts applied through the codes. For course videos, some handwritten markings are shown by narrator, which are difficult to remember. Also narration carries lot of useful information, which appears as a part of quiz. Overall very good.
2000
Buy Now
Includes:
  • Self-Paced Training
  • 10:46:37 Hours Videos
  • 119 Lessons
  • Certification Course
  • All Levels