Certificate Program in Data Science & Advanced Machine Learning using Python

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

paid 0(0 Ratings) 5 Students Enrolled
Created By Imurgence i Last Updated Mon, 11-Feb-2019
+ View More
Description
This is an advanced course by Imurgence using Python, which dives deep into an introduction to data analytics, Python IDE, Python basics, Python packages, basic statistics, linear and logistic regression, decision tree, ensemble learning, support vector machines, k-nearest neighbours, clustering and artificial neural network.

  • Upon successful completion of this course, the learner will be skilled in Python programming to perform data analytics on business data, build predictive machine learning models.

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
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 successful completion of this course, the learner will be sent a digital copy of the certificate to their email.



Curriculum For This Course
246 Lessons 09:23:10 Hours
Introduction to Data Analytics
3 Lessons 00:31:37 Hours
  • Overview and Scope of Analytics 1.1 00:10:47
  • Overview and Scope of Analytics 1.2 00:08:26
  • Overview and Scope of Analytics 1.3 00:12:24
  • Installation of Python 2.1 00:07:01
  • How to use Python 2.2 00:07:33
  • How to use Python 2.3 00:04:40
  • Installing Packages 2.4 00:04:44
  • Data_Types 3.1 00:06:42
  • Data_Types 3.2 00:05:00
  • Data_Types 3.3 00:12:41
  • Data Structure - List 3.4 00:09:33
  • Data Structure - List 3.5 00:13:39
  • Data Structure - List 3.6 00:04:44
  • Data Structure - List 3.7 00:05:54
  • Data Structure - List 3.8 00:03:56
  • Data Structure - List 3.9 00:05:42
  • Data Structure - List 3.9 00:05:42
  • Data Structure - Tuple 3.10 00:08:01
  • Data Structure - Tuple 3.11 00:03:50
  • Data Structure - Tuple 3.12 00:07:42
  • Data Structure - Dictionary 3.13 00:07:21
  • Data Structure - Dictionary 3.14 00:07:32
  • Data Structure - Dictionary 3.15 00:04:56
  • Arithmetic Operators 4.1 00:06:11
  • Relational Operators 4.2 00:03:33
  • Assignment Operators 4.3 00:04:37
  • Control Statements - Loops 4.4 00:21:12
  • Control Statements - Loops 4.5 00:06:56
  • Functions 5.1 00:08:58
  • Functions 5.2 00:06:51
  • Functions 5.3 00:11:14
  • Functions 5.4 00:07:30
  • Numpy 6.1 00:05:19
  • Numpy 6.2 00:16:25
  • Numpy 6.3 00:11:47
  • Numpy 6.4 00:07:31
  • Numpy 6.5 00:05:33
  • Numpy 6.6 00:16:30
  • Numpy 6.7 00:15:06
  • Pandas 7.1 00:09:29
  • Pandas 7.2 00:07:39
  • Pandas 7.3 00:11:41
  • Pandas 7.4 00:10:29
  • Pandas 7.5 00:11:54
  • Pandas 7.6 00:07:21
  • Pandas 7.7 00:05:48
  • Pandas 7.8 00:16:01
  • Pandas 7.9 00:09:41
  • Pandas 7.10 00:06:19
  • Pandas 7.11 00:07:39
  • Pandas 7.12 00:07:49
  • Pandas 7.13 00:13:54
  • Pandas 7.14 00:05:44
  • Data Manipulation 8.1 00:09:42
  • Data Manipulation 8.2 00:09:45
  • Data Manipulation 8.3 00:06:24
  • Data Manipulation 8.4 00:09:11
  • Data Manipulation 8.5 00:21:11
  • Data Manipulation 8.6 00:10:25
  • Data Manipulation 8.7 00:11:59
  • Data Manipulation 8.8 00:10:37
  • Data Manipulation 8.9 00:08:22
  • Data Manipulation 8.10 00:10:53
  • Data Preprocessing 9.1 00:11:59
  • Data Preprocessing 9.2 00:09:11
  • Data Preprocessing 9.3 00:09:17
  • Data Preprocessing 9.4 00:08:47
  • Visualization using Matplot 10.1 00:09:18
  • Visualization using Matplot 10.2 00:06:40
  • Visualization using Matplot 10.3 00:07:47
  • Visualization using Matplot 10.4 00:03:02
  • Visualization using Matplot 10.5 00:07:21
  • Visualization using Matplot 10.6 00:08:39
  • Visualization using Matplot 10.7 00:10:13
  • Introduction To Statistics 11.1 00:01:22
  • Introduction To Statistics 11.2 00:03:44
  • Introduction To Statistics 11.3 00:06:20
  • Introduction To Statistics 11.4 00:04:54
  • Introduction To Statistics 11.5 00:03:51
  • Introduction To Statistics 11.6 00:06:10
  • Introduction To Statistics 11.7 00:07:11
  • Introduction To Statistics 11.8 00:05:11
  • Introduction To Statistics 11.9 00:02:11
  • Introduction To Statistics 11.10 00:00:58
  • Introduction To Statistics 11.11 00:04:38
  • Introduction To Statistics 11.12 00:02:26
  • Introduction To Statistics 11.13 00:04:42
  • Introduction To Statistics 11.14 00:05:51
  • Introduction To Statistics 11.15 00:04:22
  • Linear Regression 12.1 00:05:01
  • Linear Regression 12.2 00:03:27
  • Linear Regression 12.3 00:02:42
  • Linear Regression 12.4 00:04:56
  • Linear Regression 12.5 00:08:09
  • Linear Regression 12.6 00:01:56
  • Linear Regression 12.7 00:03:04
  • Linear Regression 12.8 00:02:39
  • Linear Regression 12.9 00:06:42
  • Linear Regression 12.10 00:05:45
  • Linear Regression 12.11 00:04:32
  • Linear Regression 12.12 00:08:20
  • Linear Regression 12.13 00:04:50
  • Simple Linear Regression 12.14 00:11:20
  • Simple Linear Regression 12.15 00:04:58
  • Simple Linear Regression 12.16 00:06:17
  • Simple Linear Regression 12.17 00:09:17
  • Multiple Linear Regression 12.18 00:15:45
  • Multiple Linear Regression 12.19 00:06:32
  • Multiple Linear Regression 12.20 00:06:45
  • Multiple Linear Regression 12.21 00:08:29
  • Multiple Linear Regression 12.22 00:02:50
  • Multiple Linear Regression 12.23 00:05:00
  • Multiple Linear Regression 12.24 00:08:18
  • Multiple Linear Regression on Boston Dataset 12.25 00:12:58
  • Multiple Linear Regression on Boston Dataset 12.26 00:13:06
  • Multiple Linear Regression on Boston Dataset 12.27 00:09:14
  • Multiple Linear Regression on Boston Dataset 12.28 00:09:51
  • Multiple Linear Regression on Boston Dataset 12.29 00:08:35
  • Multiple Linear Regression on Boston Dataset 12.30 00:19:18
  • Multiple Linear Regression on Boston Dataset 12.31 00:12:12
  • Logistic Regression 13.1 00:04:51
  • Logistic Regression 13.2 00:06:53
  • Logistic Regression 13.3 00:03:46
  • Logistic Regression 13.4 00:10:58
  • Logistic Regression 13.5 00:10:01
  • Logistic Regression 13.6 00:04:47
  • Logistic Regression 13.7 00:07:58
  • Logistic Regression 13.8 00:04:22
  • Logistic Regression 13.9 00:06:29
  • Logistic Regression 13.10 00:12:29
  • Logistic Regression 13.11 00:10:54
  • Logistic Regression 13.12 00:14:13
  • Logistic Regression on Diabetes Dataset 13.13 00:11:11
  • Logistic Regression on Diabetes Dataset 13.14 00:12:30
  • Logistic Regression on Diabetes Dataset 13.15 00:07:49
  • Logistic Regression on Diabetes Dataset 13.16 00:07:08
  • Logistic Regression on Diabetes Dataset 13.17 00:16:16
  • Logistic Regression on Diabetes Dataset 13.18 00:07:29
  • Logistic Regression on Diabetes Dataset 13.19 00:14:03
  • Logistic Regression on Diabetes Dataset 13.20 00:11:25
  • Credit Risk Case using Logistic Regression 13.21 00:12:00
  • Credit Risk Case using Logistic Regression 13.22 00:09:27
  • Credit Risk Case using Logistic Regression 13.23 00:08:20
  • Credit Risk Case using Logistic Regression 13.24 00:14:29
  • Credit Risk Case using Logistic Regression 13.25 00:13:08
  • Credit Risk Case using Logistic Regression 13.26 00:12:30
  • Credit Risk Case using Logistic Regression 13.27 00:08:49
  • Credit Risk Case using Logistic Regression 13.28 00:13:35
  • Credit Risk Case using Logistic Regression 13.29 00:04:36
  • Introduction to Machine Learning 14.0 00:04:44
  • Decision Tree 14.1 00:05:59
  • Decision Tree 14.2 00:06:42
  • Decision Tree 14.3 00:03:09
  • Decision Tree 14.4 00:05:57
  • Decision Tree 14.5 00:04:55
  • Decision Tree 14.6 00:03:04
  • Decision Tree 14.7 00:13:37
  • Decision Tree 14.8 00:10:28
  • Decision Tree 14.9 00:21:15
  • Decision Tree 14.10 00:12:04
  • Decision Tree 14.11 00:02:44
  • Bagging 15.1 00:08:00
  • Random_Forest 15.2 00:16:48
  • Random_Forest 15.3 00:04:50
  • Boosting 15.4 00:03:22
  • XG Boost 15.5 00:12:21
  • XG Boost 15.6 00:06:41
  • XG Boost 15.7 00:06:05
  • XG Boost 15.8 00:09:57
  • XG Boost 15.9 00:07:11
  • Random Forest on Diabetes Dataset 15.10 00:07:11
  • Random Forest on Diabetes Dataset 15.11 00:05:38
  • Random Forest on Diabetes Dataset 15.12 00:06:46
  • Random Forest on Diabetes Dataset 15.13 00:09:49
  • IT Network Intrusion Detection Case using Decision Tree 15.14 00:06:00
  • IT Network Intrusion Detection Case using Decision Tree 15.15 00:10:37
  • IT Network Intrusion Detection Case using Decision Tree 15.16 00:09:32
  • IT Network Intrusion Detection Case using Decision Tree 15.17 00:06:40
  • IT Network Intrusion Detection Case using Decision Tree 15.18 00:06:44
  • IT Network Intrusion Detection Case using Decision Tree 15.19 00:07:10
  • IT Network Intrusion Detection Case using Decision Tree 15.20 00:03:32
  • IT Network Intrusion Detection Case using Decision Tree 15.21 00:08:01
  • Support Vector Machine 16.1 00:04:00
  • Support Vector Machine 16.2 00:04:35
  • Support Vector Machine 16.3 00:03:03
  • Support Vector Machine 16.4 00:02:54
  • Support Vector Machine 16.5 00:08:42
  • Support Vector Machine 16.6 00:04:51
  • Support Vector Machine 16.7 00:04:20
  • Support Vector Machine 16.8 00:15:52
  • Support Vector Machine 16.9 00:07:14
  • Support Vector Machine 16.10 00:06:43
  • Support Vector Machine 16.11 00:13:09
  • Support Vector Machine 16.12 00:09:58
  • Support Vector Machine 16.13 00:14:55
  • Credit Risk Case Using SVM 16.14 00:09:34
  • Credit Risk Case Using SVM 16.15 00:10:46
  • Credit Risk Case Using SVM 16.16 00:11:00
  • Credit Risk Case Using SVM 16.17 00:11:40
  • Credit Risk Case Using SVM 16.18 00:14:36
  • Credit Risk Case Using SVM 16.19 00:09:50
  • Credit Risk Case Using SVM 16.20 00:11:51
  • Credit Risk Case Using SVM 16.21 00:06:57
  • K Fold Cross Validation 16.22 00:15:15
  • Market Basket Analysis 17.1 00:06:48
  • Market Basket Analysis 17.2 00:06:12
  • Market Basket Analysis 17.3 00:03:06
  • Market Basket Analysis 17.4 00:02:35
  • French Store Analysis Using MBA 17.5 00:04:50
  • French Store Analysis Using MBA 17.6 00:13:55
  • French Store Analysis Using MBA 17.7 00:03:31
  • k Nearest Neighbours 18.1 00:07:31
  • k Nearest Neighbours 18.2 00:03:36
  • k Nearest Neighbours 18.3 00:05:09
  • kNN on Advertisement Dataset 18.4 00:13:20
  • kNN on Advertisement Dataset 18.5 00:05:12
  • Customer Churn using kNN 18.6 00:08:52
  • Customer Churn using kNN 18.7 00:07:04
  • Customer Churn using kNN 18.8 00:08:58
  • kNN on Cancer Dataset 18.9 00:10:50
  • kNN on Cancer Dataset 18.10 00:08:41
  • kMeans 19.1 00:03:18
  • kMeans 19.2 00:05:45
  • kMeans 19.3 00:03:42
  • kMeans 19.4 00:04:24
  • kMeans 19.5 00:06:30
  • kMeans Customer Segementation Case 19.6 00:06:59
  • kMeans Customer Segementation Case 19.7 00:09:44
  • kMeans Customer Segementation Case 19.8 00:14:30
  • Artificial Neural Network 20.1 00:01:52
  • Artificial Neural Network 20.2 00:07:35
  • Artificial Neural Network 20.3 00:06:14
  • Artificial Neural Network 20.4 00:02:04
  • Artificial Neural Network 20.5 00:03:59
  • Artificial Neural Network 20.6 00:06:41
  • Artificial Neural Network 20.7 00:06:42
  • Artificial Neural Network 20.8 00:03:32
  • Artificial Neural Network 20.9 00:08:28
  • Artificial Neural Network 20.10 00:15:31
  • Artificial Neural Network 20.11 00:11:24
  • Artificial Neural Network 20.12 00:08:17
  • Artificial Neural Network 20.13 00:05:57
  • Bank Customer Churn Case Using ANN 20.14 00:10:48
  • Bank Customer Churn Case Using ANN 20.15 00:12:26
  • Bank Customer Churn Case Using ANN 20.16 00:08:39
  • Bank Customer Churn Case Using ANN 20.17 00:13:42
+ View More
Other Related Courses
About The Instructor
  • 1 Reviews
  • 62 Students
  • 18 Courses
+ View More
A Data Science Training Company
Powered by Simple & Real Analytics
Student Feedback
0
Average Rating
  • 0%
  • 0%
  • 0%
  • 0%
  • 0%
Reviews
5000 6000
Buy Now
Includes:
  • Self-Paced Training
  • 09:23:10 Hours Videos
  • 246 Lessons
  • Certification Course
  • All Levels