iDSLive : 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 in LIVE Virtual Class room on One on One Basis

paid 0 Students Enrolled
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, iDS our flagship course which comprises 6 modules, 3 modules of R and 3 modules of Python. Its a 40 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 & 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.


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 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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