Certificate Program in Data Science & Advanced Machine Learning using R ( Interactive Mode )

Learn concepts of data analytics, data science and advanced machine learning using R with hands-on case study in LIVE Virtual Class room on One on One Basis

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Created By Imurgence Learning Last Updated Thu, 19-Dec-2019
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Description
The course is an LIVE  online virtual course and will be conducted on 1 to 1 basis, Our Course comprises of 3 modules of R. Its a 20 Hour module with constant access to Faculty Mr. Mohan Rai  as a trainer through a Web Conferencing and Screen sharing platform. LIVE Chat Support with the Faculty is for asking and resolving queries while the session is not underway. 

  • Upon successful completion of this course, the learner will be skilled in Machine Learning and AI using R.

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.


Prerequisites
There are no prerequisites for this course, but a general understanding of statistics and an inclination to learn coding would benefit the learner.

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.



Curriculum For This Course
0 Lessons 0:00:00 Hours
Introduction to Data Analytics
6 Lessons 00:40:33 Hours
  • Overview and Scope of Analytics 1.1 00:10:57
  • Overview and Scope of Analytics 1.2 00:08:26
  • Overview and Scope of Analytics 1.3 00:12:24
  • Introduction to R 1.4 00:04:14
  • Introduction to R 1.5 00:02:02
  • Application of Analytics 1.6 00:02:30
  • R Base and R Studio Installation 2.1 00:04:32
  • How to Use R Studio 2.2 00:02:34
  • How to Use R Studio 2.3 00:08:56
  • Data Types 3.1 00:05:14
  • Data Types 3.2 00:06:33
  • Data Types 3.3 00:04:01
  • Data Types 3.4 00:07:42
  • Data Types 3.5 00:05:48
  • Data Types 3.6 00:09:25
  • Arithmetic Operators in R 3.7 00:04:54
  • Relational Operators in R 3.8 00:06:24
  • Logical Operators in R 3.9 00:09:12
  • Assignment Operators in R 3.10 00:04:09
  • Matrix 3.11 00:05:54
  • Matrix 3.12 00:02:01
  • Matrix 3.13 00:06:40
  • Matrix 3.14 00:06:20
  • Matrix 3.15 00:12:19
  • Factors 3.16 00:04:56
  • Factors 3.17 00:06:59
  • Factors 3.18 00:09:31
  • Factors 3.19 00:03:37
  • Lists 3.20 00:04:51
  • Lists 3.21 00:07:08
  • DataFrames 3.22 00:04:46
  • DataFrames 3.23 00:08:04
  • DataFrames 3.24 00:05:13
  • DataFrames 3.25 00:09:02
  • if statement 4.1 00:06:18
  • if else statement 4.2 00:13:17
  • Switch Statement 4.3 00:04:05
  • Repeat Loop 4.4 00:05:47
  • While loop 4.5 00:03:17
  • For loop 4.6 00:02:13
  • Apply function 5.1 00:04:02
  • Apply Function 5.2 00:07:53
  • Apply Function 5.3 00:04:27
  • Apply Function 5.4 00:02:14
  • Lapply Function 5.5 00:04:41
  • Sapply Function 5.6 00:10:08
  • Vapply Function 5.7 00:02:26
  • Tapply Function 5.8 00:03:57
  • User-defined Functions 5.9 00:07:32
  • User-defined Functions 5.10 00:08:32
  • Import and Export of Data 6.1 00:05:15
  • Sample Function 6.2 00:10:55
  • Data Exploration in R 6.3 00:06:36
  • Data Exploration in R 6.4 00:09:43
  • Data Exploration in R 6.5 00:05:44
  • Missing Values 6.6 00:08:49
  • Basic Plot 7.1 00:08:04
  • Plotting Categorical Data 7.2 00:04:54
  • Histogram 7.3 00:10:14
  • Pie Chart 7.4 00:09:40
  • Introduction To Statistics 8.1 00:01:22
  • Introduction To Statistics 8.2 00:03:44
  • Introduction To Statistics 8.3 00:06:20
  • Introduction to Statistics 8.4 00:04:54
  • Introduction To Statistics 8.5 00:03:51
  • Introduction To Statistics 8.6 00:06:10
  • Introduction To Statistics 8.7 00:07:11
  • Introduction To Statistics 8.8 00:05:11
  • Introduction To Statistics 8.9 00:02:11
  • Introduction To Statistics 8.10 00:00:58
  • Introduction To Statistics 8.11 00:04:38
  • Introduction To Statistics 8.12 00:02:26
  • Introduction To Statistics 8.13 00:04:42
  • Introduction To Statistics 8.14 00:05:51
  • Introduction To Statistics 8.15 00:04:22
  • Probability Theory 9.1 00:06:07
  • Probability Theory 9.2 00:06:02
  • Probability Theory 9.3 00:04:49
  • Probability Theory 9.4 00:06:19
  • Probability Theory 9.5 00:06:18
  • Inferential Statistics 10.1 00:01:53
  • Inferential Statistics 10.2 00:01:28
  • Inferential Statistics 10.3 00:04:27
  • Inferential Statistics 10.4 00:04:14
  • Inferential Statistics 10.5 00:08:20
  • Inferential Statistics 10.6 00:04:26
  • Inferential Statistics 10.7 00:06:09
  • Inferential Statistics 10.8 00:06:21
  • Inferential Statistics 10.9 00:05:09
  • Inferential Statistics 10.10 00:06:37
  • Inferential Statistics 10.11 00:02:38
  • Inferential Statistics 10.12 00:03:32
  • Inferential Statistics 10.13 00:03:58
  • Inferential Statistics 10.14 00:01:46
  • Inferential Statistics 10.15 00:05:57
  • Linear Regression 11.1 00:05:01
  • Linear Regression 11.2 00:03:27
  • Linear Regression 11.3 00:02:42
  • Linear Regression 11.4 00:04:56
  • Linear Regression 11.5 00:08:09
  • Linear Regression 11.6 00:01:56
  • Linear Regression 11.7 00:03:04
  • Linear Regression 11.8 00:02:39
  • Linear Regression 11.9 00:06:42
  • Linear Regression 11.10 00:05:45
  • Simple Linear Regression 11.11 00:04:22
  • Simple Linear Regression 11.12 00:05:45
  • Simple Linear Regression 11.13 00:07:18
  • Simple Linear Regression 11.14 00:04:46
  • Simple Linear Regression 11.15 00:07:46
  • Simple Linear Regression 11.16 00:10:57
  • Dummies 11.17 00:05:33
  • Multiple Linear Regression 11.18 00:05:20
  • Multiple Linear Regression 11.19 00:03:14
  • Model Optimisation 11.20 00:07:45
  • Model Optimization 11.21 00:04:34
  • Multiple Linear Regression 11.22 00:08:17
  • Multiple Linear Regression 11.23 00:06:15
  • Multiple Linear Regression on Boston Dataset 11.24 00:07:59
  • Multiple Linear Regression on Boston Dataset 11.25 00:06:52
  • Multiple Linear Regression on Boston Dataset 11.26 00:09:20
  • Multiple Linear Regression on Boston Dataset 11.27 00:04:03
  • Multiple Linear Regression on Boston Dataset 11.28 00:07:25
  • Multiple Linear Regression on Boston Dataset 11.29 00:08:24
  • Multiple Linear Regression on Boston Dataset 11.30 00:04:05
  • Logistic Regression 12.1 00:04:51
  • Logistic Regression 12.2 00:06:53
  • Logistic Regression 12.3 00:03:46
  • Logistic Regression 12.4 00:10:58
  • Logistic Regression 12.5 00:10:01
  • Logistic Regression 12.6 00:04:47
  • Logistic Regression 12.7 00:07:58
  • Logistic Regression 12.8 00:04:22
  • Logistic Regression 12.9 00:06:29
  • Logistic Regression 12.10 00:07:34
  • Logistic Regression 12.11 00:08:15
  • Logistic Regression 12.12 00:03:07
  • Logistic Regression 12.13 00:07:41
  • Logistic Regression on Diabetes Dataset 12.14 00:05:25
  • Logistic Regression on Diabetes Dataset 12.15 00:07:41
  • Logistic Regression on Diabetes Dataset 12.16 00:10:08
  • Logistic Regression on Diabetes Dataset 12.17 00:03:11
  • Logistic Regression on Diabetes Dataset 12.18 00:03:11
  • Logistic Regression on Diabetes Dataset 12.19 00:07:42
  • Logistic Regression on Diabetes Dataset 12.20 00:06:46
  • Logistic Regression on Diabetes Dataset 12.21 00:02:50
  • Credit Risk Case using Logistic Regression 12.22 00:04:34
  • Credit Risk Case using Logistic Regression 12.23 00:01:51
  • Credit Risk Case using Logistic Regression 12.24 00:02:24
  • Credit Risk Case using Logistic Regression 12.25 00:05:18
  • Credit Risk Case using Logistic Regression 12.26 00:05:42
  • Credit Risk Case using Logistic Regression 12.27 00:04:50
  • Credit Risk Case using Logistic Regression 12.28 00:10:54
  • Credit Risk Case using Logistic Regression 12.29 00:06:48
  • Credit Risk Case using Logistic Regression 12.30 00:03:39
  • Credit Risk Case using Logistic Regression 12.31 00:05:56
  • Credit Risk Case using Logistic Regression 12.32 00:03:25
  • Credit Risk Case using Logistic Regression 12.33 00:05:18
  • Credit Risk Case using Logistic Regression 12.34 00:05:42
  • Credit Risk Case using Logistic Regression 12.35 00:04:43
  • Credit Risk Case using Logistic Regression 12.36 00:05:52
  • ROC Curve 12.37 00:04:06
  • Introduction to Machine Learning 13.0 00:04:44
  • Decision Tree 13.1 00:05:59
  • Decision Tree 13.2 00:06:42
  • Decision Tree 13.3 00:03:09
  • Decision Tree 13.4 00:05:57
  • Decision Tree 13.5 00:04:55
  • Decision Tree 13.6 00:03:04
  • Decision Tree 13.7 00:06:39
  • Decision Tree 13.8 00:09:28
  • Decision Tree 13.9 00:09:18
  • Decision Tree on Diabetes Dataset 13.10 00:03:54
  • Decision Tree on Diabetes Dataset 13.11 00:06:59
  • Decision Tree on Diabetes Dataset 13.12 00:02:30
  • Random Forest 14.1 00:08:00
  • Random Forest 14.2 00:03:22
  • Random Forest 14.3 00:05:39
  • Random Forest 14.4 00:05:23
  • Random Forest 14.5 00:06:37
  • Random Forest on Diabetes Dataset 14.6 00:03:56
  • Random Forest on Diabetes Dataset 14.7 00:06:59
  • Random Forest on Diabetes Dataset 14.8 00:02:30
  • Random Forest on Diabetes Dataset 14.9 00:05:26
  • Random Forest Regression 14.10 00:04:27
  • Random Forest Regression 14.11 00:05:17
  • Random Forest Regression 14.12 00:01:01
  • Random Forest Regression 14.13 00:04:50
  • Random Forest Regression 14.14 00:05:32
  • Random Forest Regression 14.15 00:02:56
  • IT Network Intrusion Detection Case using Decision Tree 14.16 00:05:37
  • IT Network Intrusion Detection Case using Decision Tree 14.17 00:07:30
  • IT Network Intrusion Detection Case using Decision Tree 14.18 00:04:39
  • IT Network Intrusion Detection Case using Decision Tree 14.19 00:03:01
  • IT Network Intrusion Detection Case using Decision Tree 14.20 00:07:44
  • IT Network Intrusion Detection Case using Decision Tree 14.21 00:03:02
  • IT Network Intrusion Detection Case using Decision Tree 14.22 00:08:12
  • IT Network Intrusion Detection Case using Decision Tree 14.23 00:04:37
  • IT Network Intrusion Detection Case using Decision Tree 14.24 00:04:14
  • Support Vector Machine 15.1 00:04:00
  • Support Vector Machine 15.2 00:04:35
  • Support Vector Machine 15.3 00:04:44
  • Support Vector Machine 15.4 00:02:54
  • Support Vector Machine 15.5 00:08:42
  • Support Vector Machine 15.6 00:04:51
  • Support Vector Machine 15.7 00:04:20
  • Support Vector Machine 15.8 00:06:18
  • Support Vector Machine 15.9 00:05:35
  • Support Vector Machine 15.10 00:02:59
  • Support Vector Machine 15.11 00:03:11
  • Support Vector Machine 15.12 00:03:29
  • Support Vector Machine 15.13 00:03:03
  • SVM on Iris Dataset 15.14 00:03:45
  • SVM on Iris Dataset 15.15 00:08:13
  • SVM on Iris Dataset 15.16 00:07:08
  • SVM on Iris Dataset 15.17 00:07:12
  • SVM on Iris Dataset 15.18 00:04:30
  • SVM on Iris Dataset 15.19 00:04:07
  • Credit Risk Case Using SVM 15.20 00:04:02
  • Credit Risk Case Using SVM 15.21 00:04:08
  • Credit Risk Case Using SVM 15.22 00:05:13
  • Credit Risk Case Using SVM 15.23 00:06:50
  • Credit Risk Case Using SVM 15.24 00:07:25
  • K Fold Cross Validation 15.25 00:04:16
  • K Fold Cross Validation 15.26 00:09:09
  • K Fold Cross Validation 15.27 00:02:10
  • K Fold Cross Validation 15.28 00:07:28
  • Market Basket Analysis 16.1 00:06:48
  • Market Basket Analysis 16.2 00:06:12
  • Market Basket Analysis 16.3 00:03:06
  • Market Basket Analysis 16.4 00:02:35
  • French Store Analysis Using MBA 16.5 00:05:34
  • French Store Analysis Using MBA 16.6 00:05:10
  • French Store Analysis Using MBA 16.7 00:03:36
  • French Store Analysis Using MBA 16.8 00:08:37
  • French Store Analysis Using MBA 16.9 00:05:32
  • k Nearest Neighbours 17.1 00:07:31
  • k Nearest Neighbours 17.2 00:03:36
  • k Nearest Neighbours 17.3 00:05:09
  • kNN on Advertisement Dataset 17.4 00:04:50
  • kNN on Advertisement Dataset 17.5 00:05:19
  • kNN on Advertisement Dataset 17.6 00:04:16
  • kNN on Cancer Dataset 17.7 00:02:42
  • kNN on Cancer Dataset 17.8 00:06:17
  • kNN on Cancer Dataset 17.9 00:03:48
  • kNN on Cancer Dataset 17.10 00:04:43
  • kNN on Cancer Dataset 17.11 00:05:38
  • kNN on Cancer Dataset 17.12 00:09:03
  • Customer Churn using kNN 17.13 00:11:11
  • Customer Churn using kNN 17.14 00:06:50
  • Customer Churn using kNN 17.15 00:06:12
  • Customer Churn using kNN 17.16 00:10:32
  • kMeans 18.1 00:03:18
  • kMeans 18.2 00:05:45
  • kMeans 18.3 00:03:42
  • kMeans 18.4 00:04:24
  • kMeans 18.5 00:06:30
  • kMeans Customer Segmentation Case 18.6 00:03:35
  • kMeans Customer Segmentation Case 18.7 00:06:26
  • kMeans Customer Segmentation Case 18.8 00:04:15
  • kMeans Customer Segmentation Case 18.9 00:06:05
  • kMeans Customer Segmentation Case 18.10 00:03:30
  • Artificial Neural Network 19.1 00:01:52
  • Artificial Neural Network 19.2 00:07:35
  • Artificial Neural Network 19.3 00:06:14
  • Artificial Neural Network 19.4 00:02:04
  • Artificial Neural Network 19.5 00:03:59
  • Artificial Neural Network 19.6 00:06:41
  • Artificial Neural Network 19.7 00:06:42
  • Artificial Neural Network 19.8 00:03:32
  • Artificial Neural Network 19.9 00:08:28
  • Artificial Neural Network 19.10 00:15:31
  • Artificial Neural Network 19.11 00:11:24
  • Artificial Neural Network 19.12 00:08:17
  • Artificial Neural Network 19.13 00:05:57
  • Bank Customer Churn Case Using ANN 19.14 00:05:35
  • Bank Customer Churn Case Using ANN 19.15 00:04:20
  • Bank Customer Churn Case Using ANN 19.16 00:05:31
  • Bank Customer Churn Case Using ANN 19.17 00:05:38
  • Bank Customer Churn Case Using ANN 19.18 00:05:09
  • Bank Customer Churn Case Using ANN 19.19 00:01:55
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