iDS : Certificate Program in Data Science & Advanced Machine Learning using R & Python

Learn concepts of data analytics, data science and advanced machine learning using R and Python with hands-on case study

paid 5(1 Ratings) 4 Students Enrolled
Created By Imurgence Learning Last Updated Thu, 18-Apr-2019
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Description
This is a comprehensive Self-Paced Online course, which covers R and Python enabling Participants to dive deep into Data and Business Analytics. This 60-hour certificate course can be accessed 24/7 through our LMS (Learning Management System), the study will reveal finer concepts of Advanced Machine Learning concepts to the learner.Our Testimonial are given by genuine students, who were given placement assistance by Imurgence.

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

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
One Year from the date of access.

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.

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
449 Lessons 52:39:36 Hours
Introduction to Data Analytics using R
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 12.7 00:07:58
  • 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 12.8 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
  • Installation of Python 20.1 00:07:01
  • How to use Python 20.2 00:07:33
  • How to use Python 20.3 00:04:40
  • Installing Packages 20.4 00:04:44
  • Data_Types 21.1 00:06:42
  • Data_Types 21.2 00:05:00
  • Data_Types 21.3 00:12:41
  • Data Structure - List 21.4 00:09:33
  • Data Structure - List 21.5 00:13:39
  • Data Structure - List 21.6 00:04:44
  • Data Structure - List 21.7 00:05:54
  • Data Structure - List 21.8 00:03:56
  • Data Structure - List 21.9 00:05:42
  • Data Structure - Tuple 21.10 00:08:01
  • Data Structure - Tuple 21.11 00:03:50
  • Data Structure - Tuple 21.12 00:07:42
  • Data Structure - Dictionary 21.13 00:07:21
  • Data Structure - Dictionary 21.14 00:07:32
  • Data Structure - Dictionary 21.15 00:04:56
  • Arithmetic Operators 22.1 00:06:11
  • Relational Operators 22.2 00:03:33
  • Assignment Operators 22.3 00:04:37
  • Control Statements - Loops 22.4 00:21:12
  • Control Statements - Loops 22.5 00:06:56
  • Functions 23.1 00:08:58
  • Functions 23.2 00:06:51
  • Functions 23.3 00:11:14
  • Functions 23.4 00:07:30
  • Numpy 24.1 00:05:19
  • Numpy 24.2 00:16:25
  • Numpy 24.3 00:11:47
  • Numpy 24.4 00:07:31
  • Numpy 24.5 00:05:33
  • Numpy 24.6 00:16:30
  • Numpy 24.7 00:15:06
  • Pandas 25.1 00:09:29
  • Pandas 25.2 00:07:39
  • Pandas 25.3 00:11:41
  • Pandas 25.4 00:10:29
  • Pandas 25.5 00:11:54
  • Pandas 25.6 00:07:21
  • Pandas 25.7 00:05:48
  • Pandas 25.8 00:16:01
  • Pandas 25.9 00:09:41
  • Pandas 25.10 00:06:19
  • Pandas 25.11 00:07:39
  • Pandas 25.12 00:07:49
  • Pandas 25.13 00:13:54
  • Pandas 25.14 00:05:44
  • Data Manipulation 26.1 00:09:42
  • Data Manipulation 26.2 00:09:45
  • Data Manipulation 26.3 00:06:24
  • Data Manipulation 26.4 00:09:11
  • Data Manipulation 26.5 00:21:11
  • Data Manipulation 26.6 00:10:25
  • Data Manipulation 26.7 00:11:59
  • Data Manipulation 26.8 00:10:37
  • Data Manipulation 26.9 00:08:22
  • Data Manipulation 26.10 00:10:53
  • Data Preprocessing 27.1 00:11:59
  • Data Preprocessing 27.2 00:09:11
  • Data Preprocessing 27.3 00:09:17
  • Data Preprocessing 27.4 00:08:47
  • Visualization using Matplot 28.1 00:09:18
  • Visualization using Matplot 28.2 00:06:40
  • Visualization using Matplot 28.3 00:07:47
  • Visualization using Matplot 28.4 00:03:02
  • Visualization using Matplot 28.5 00:07:21
  • Visualization using Matplot 28.6 00:08:39
  • Visualization using Matplot 28.7 00:10:13
  • Linear Regression 29.1 00:04:32
  • Linear Regression 29.2 00:08:20
  • Linear Regression 29.3 00:04:50
  • Simple Linear Regression 29.4 00:11:20
  • Simple Linear Regression 29.5 00:04:58
  • Simple Linear Regression 29.6 00:06:17
  • Simple Linear Regression 29.7 00:09:17
  • Multiple Linear Regression 29.8 00:15:45
  • Multiple Linear Regression 29.9 00:06:32
  • Multiple Linear Regression 29.10 00:06:45
  • Multiple Linear Regression 29.11 00:08:29
  • Multiple Linear Regression 29.12 00:02:50
  • Multiple Linear Regression 29.13 00:05:00
  • Multiple Linear Regression 29.14 00:08:18
  • Multiple Linear Regression on Boston Dataset 29.15 00:12:58
  • Multiple Linear Regression on Boston Dataset 29.16 00:13:06
  • Multiple Linear Regression on Boston Dataset 29.17 00:09:14
  • Multiple Linear Regression on Boston Dataset 29.18 00:09:51
  • Multiple Linear Regression on Boston Dataset 29.19 00:08:35
  • Multiple Linear Regression on Boston Dataset 29.20 00:19:18
  • Multiple Linear Regression on Boston Dataset 29.21 00:12:12
  • Logistic Regression 30.1 00:12:29
  • Logistic Regression 30.2 00:10:54
  • Logistic Regression 30.3 00:14:13
  • Logistic Regression on Diabetes Dataset 30.4 00:11:11
  • Logistic Regression on Diabetes Dataset 30.5 00:12:30
  • Logistic Regression on Diabetes Dataset 30.6 00:07:49
  • Logistic Regression on Diabetes Dataset 30.7 00:07:08
  • Logistic Regression on Diabetes Dataset 30.8 00:16:16
  • Logistic Regression on Diabetes Dataset 30.9 00:07:29
  • Logistic Regression on Diabetes Dataset 30.10 00:14:03
  • Logistic Regression on Diabetes Dataset 30.11 00:11:25
  • Credit Risk Case using Logistic Regression 30.12 00:12:00
  • Credit Risk Case using Logistic Regression 30.13 00:09:27
  • Credit Risk Case using Logistic Regression 30.14 00:08:20
  • Credit Risk Case using Logistic Regression 30.15 00:14:29
  • Credit Risk Case using Logistic Regression 30.16 00:13:08
  • Credit Risk Case using Logistic Regression 30.17 00:12:30
  • Credit Risk Case using Logistic Regression 30.18 00:08:49
  • Credit Risk Case using Logistic Regression 30.19 00:13:35
  • Credit Risk Case using Logistic Regression 30.20 00:04:36
  • Decision Tree 31.1 00:13:37
  • Decision Tree 31.2 00:10:28
  • Decision Tree 31.3 00:21:15
  • Decision Tree 31.4 00:12:04
  • Decision Tree 31.5 00:02:44
  • Random_Forest 32.1 00:16:48
  • Random_Forest 32.2 00:04:50
  • XG Boost 32.3 00:12:21
  • XG Boost 32.4 00:06:41
  • XG Boost 32.5 00:06:05
  • XG Boost 32.6 00:09:57
  • XG Boost 32.7 00:07:11
  • Random Forest on Diabetes Dataset 32.8 00:07:11
  • Random Forest on Diabetes Dataset 32.9 00:05:38
  • Random Forest on Diabetes Dataset 32.10 00:06:46
  • Random Forest on Diabetes Dataset 32.11 00:09:49
  • IT Network Intrusion Detection Case using Decision Tree 32.12 00:06:00
  • IT Network Intrusion Detection Case using Decision Tree 32.13 00:10:37
  • IT Network Intrusion Detection Case using Decision Tree 32.14 00:09:32
  • IT Network Intrusion Detection Case using Decision Tree 32.15 00:06:40
  • IT Network Intrusion Detection Case using Decision Tree 32.16 00:06:44
  • IT Network Intrusion Detection Case using Decision Tree 32.17 00:07:10
  • IT Network Intrusion Detection Case using Decision Tree 32.18 00:03:32
  • IT Network Intrusion Detection Case using Decision Tree 32.19 00:08:01
  • Support Vector Machine 33.1 00:03:03
  • Support Vector Machine 33.2 00:15:52
  • Support Vector Machine 33.3 00:07:14
  • Support Vector Machine 33.4 00:06:43
  • Support Vector Machine 33.5 00:13:09
  • Support Vector Machine 33.6 00:09:58
  • Support Vector Machine 33.7 00:14:55
  • Credit Risk Case Using SVM 33.8 00:09:34
  • Credit Risk Case Using SVM 33.9 00:10:46
  • Credit Risk Case Using SVM 33.10 00:11:00
  • Credit Risk Case Using SVM 33.11 00:11:40
  • Credit Risk Case Using SVM 33.12 00:14:36
  • Credit Risk Case Using SVM 33.13 00:09:50
  • Credit Risk Case Using SVM 33.14 00:11:51
  • Credit Risk Case Using SVM 33.15 00:06:57
  • K Fold Cross Validation 33.16 00:15:15
  • French Store Analysis Using MBA 34.1 00:04:50
  • French Store Analysis Using MBA 34.2 00:13:55
  • French Store Analysis Using MBA 34.3 00:03:31
  • kNN on Advertisement Dataset 35.1 00:13:20
  • kNN on Advertisement Dataset 35.2 00:05:12
  • Customer Churn using kNN 35.3 00:08:52
  • Customer Churn using kNN 35.4 00:07:04
  • Customer Churn using kNN 35.5 00:08:58
  • kNN on Cancer Dataset 35.6 00:10:50
  • kNN on Cancer Dataset 35.7 00:08:41
  • kMeans Customer Segmentation Case 36.1 00:06:59
  • kMeans Customer Segmentation Case 36.2 00:09:44
  • kMeans Customer Segmentation Case 36.3 00:14:30
  • Bank Customer Churn Case Using ANN 37.1 00:10:48
  • Bank Customer Churn Case Using ANN 37.2 00:12:26
  • Bank Customer Churn Case Using ANN 37.3 00:08:39
  • Bank Customer Churn Case Using ANN 37.4 00:13:42
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    Kriti Mehra
    Very realistic course, starts from a complete assumption of learner as absolutely new to the concept. Gradually the complexity and engagement increases. In once course I have got exposure to many case-lets. Although from a Engineering background , I could understand the Business cases embedded along with the analytics part. Thoroughly recommended. The Live Tech Support has been very useful along with the Email Support. The Platform has integrated Project Submission which makes submissions very easy .
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