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 72 Students Enrolled
Created By Imurgence Learning Last Updated Wed, 25-Sep-2019
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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.
Free Courses Included

  • 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
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
246 Lessons 33:23:10 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
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Student Feedback
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Reviews
  • Sat, 31-Aug-2019
    Harmil Das
    This course really helped me get a good idea about how machine learning works and how artificial neural networks becomes an integral part in developing models. Imurgence team has supported me a lot, and I would like to extend my special thanks to the technical support team for resolving my doubts regarding topics and sub modules.
  • Wed, 04-Sep-2019
    Baloo Naran
    I was familiar with fundamentals of python and Statistics, hence I was searching for brief course which gives overview of data science & ML. The course is online,however, their Technical Chat group makes you experience guided learning environment. Thank you sir!
  • Thu, 05-Sep-2019
    Shauree Rastogi
    This is one of the best courses I wanted and luckily I got Imurgence data science training institute. The course delivers the materials in a concise, clear and thoughtful manner. A training is not just about delivering content but also about effectively engaging students for them to understand quickly. I would recommend this course for everyone.
  • Mon, 09-Sep-2019
    Prutvi Gupta
    Data Science is a very vast topic and covering all of its topics in one course is a challenge. This course though tries to touch upon all areas including all the algorithms of Machine learning and covers topics like ensemble learning, Artificial Neural Networks etc. The course gives good overview of data science with examples and practice. I recommend it to get a good grasp on concepts.
  • Wed, 11-Sep-2019
    Abhyank Mishra
    This course encompasses to the point information about machine learning, data science, ANN. The examples provided were useful and applicable to real world problems. The course covers content that would be useful for beginners as well as those with more experience.
  • Fri, 13-Sep-2019
    Arundhti Krishan
    This was a very informative course. The main topics like data manipulation, decision tree, SVM, statistics, etc. are well explained. I would highly recommend this for my friends and colleagues. I completed this course and now I am doing job in MNC.
  • Fri, 13-Sep-2019
    Knika Jain
    Very comprehensive introduction to the ML and data science concepts . Pretty good course!
  • Mon, 16-Sep-2019
    Kushit Malik
    Still loving the course. The training materials are invaluable and tutor is doing a great job of describing the theory. I had difficulties in understanding of ANN, but their Technical chat group helped me greatly in comprehending it. I enjoyed learning market basket analysis, clustering, linear and logistic regression, etc effortlessly. Thank you Imurgence!
  • Mon, 16-Sep-2019
    Anthra Srivas
    This course is absolutely perfect for those who are seeking a solid introduction about ML and data science using python. The modules are well structured, no other institute has designed this course the way Imurgence did. If you want valuable ML and DS course I will say go for this course.
  • Tue, 17-Sep-2019
    Aabhi Soni
    It's a good intro to machine learning. It's a general overview, which is what I wanted. The course is well organized. Examples are good. Overall I will give 5 star rating for this course. I will recommend this course at high level.
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Includes:
  • Self-Paced Training
  • 33:23:10 Hours Videos
  • 246 Lessons
  • Certification Course
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