Certificate Program in Machine Learning using Python

Learn concepts of advanced machine learning using Python with hands-on case studies

paid 0(0 Ratings) 3 Students Enrolled
Created By Imurgence Learning Last Updated Thu, 18-Apr-2019
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Description
This Imurgence certificate course comprises learning on 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 Machine Learning using 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
Six months from the date of access.

Prerequisites
The prerequisites for this course would be Python Programming. We recommend Certificate Program in Data Analytics using Python as a Prerequisite.

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
97 Lessons 12:51:11 Hours
Decision Tree
12 Lessons 01:34:38 Hours
  • Introduction to Machine Learning 1.0 00:04:44
  • Decision Tree 1.1 00:05:59
  • Decision Tree 1.2 00:06:42
  • Decision Tree 1.3 00:03:09
  • Decision Tree 1.4 00:05:57
  • Decision Tree 1.5 00:04:55
  • Decision Tree 1.6 00:03:04
  • Decision Tree 1.7 00:13:37
  • Decision Tree 1.8 00:10:28
  • Decision Tree 1.9 00:21:15
  • Decision Tree 1.10 00:12:04
  • Decision Tree 1.11 00:02:44
  • Bagging 2.1 00:08:00
  • Random_Forest 2.2 00:16:48
  • Random_Forest 2.3 00:04:50
  • Boosting 2.4 00:03:22
  • XG Boost 2.5 00:12:21
  • XG Boost 2.6 00:06:41
  • XG Boost 2.7 00:06:05
  • XG Boost 2.8 00:09:57
  • XG Boost 2.9 00:07:11
  • Random Forest on Diabetes Dataset 2.10 00:07:11
  • Random Forest on Diabetes Dataset 2.11 00:05:38
  • Random Forest on Diabetes Dataset 2.12 00:06:46
  • Random Forest on Diabetes Dataset 2.13 00:09:49
  • IT Network Intrusion Detection Case using Decision Tree 2.14 00:06:00
  • IT Network Intrusion Detection Case using Decision Tree 2.15 00:10:37
  • IT Network Intrusion Detection Case using Decision Tree 2.16 00:09:32
  • IT Network Intrusion Detection Case using Decision Tree 2.17 00:06:40
  • IT Network Intrusion Detection Case using Decision Tree 2.18 00:06:44
  • IT Network Intrusion Detection Case using Decision Tree 2.19 00:07:10
  • IT Network Intrusion Detection Case using Decision Tree 2.20 00:03:32
  • IT Network Intrusion Detection Case using Decision Tree 2.21 00:08:01
  • Support Vector Machine 3.1 00:04:00
  • Support Vector Machine 3.2 00:04:35
  • Support Vector Machine 3.3 00:03:03
  • Support Vector Machine 3.4 00:02:54
  • Support Vector Machine 3.5 00:08:42
  • Support Vector Machine 3.6 00:04:51
  • Support Vector Machine 3.7 00:04:20
  • Support Vector Machine 3.8 00:15:52
  • Support Vector Machine 3.9 00:07:14
  • Support Vector Machine 3.10 00:06:43
  • Support Vector Machine 3.11 00:13:09
  • Support Vector Machine 3.12 00:09:58
  • Support Vector Machine 3.13 00:14:55
  • Credit Risk Case Using SVM 3.14 00:09:34
  • Credit Risk Case Using SVM 3.15 00:10:46
  • Credit Risk Case Using SVM 3.16 00:11:00
  • Credit Risk Case Using SVM 3.17 00:11:40
  • Credit Risk Case Using SVM 3.18 00:14:36
  • Credit Risk Case Using SVM 3.19 00:09:50
  • Credit Risk Case Using SVM 3.20 00:11:51
  • Credit Risk Case Using SVM 3.21 00:06:57
  • K Fold Cross Validation 3.22 00:15:15
  • Market Basket Analysis 4.1 00:06:48
  • Market Basket Analysis 4.2 00:06:12
  • Market Basket Analysis 4.3 00:03:06
  • Market Basket Analysis 4.4 00:02:35
  • French Store Analysis Using MBA 4.5 00:07:50
  • French Store Analysis Using MBA 4.6 00:13:55
  • French Store Analysis Using MBA 4.7 00:03:31
  • k Nearest Neighbours 5.1 00:07:31
  • k Nearest Neighbours 5.2 00:03:36
  • k Nearest Neighbours 5.3 00:05:09
  • kNN on Advertisement Dataset 5.4 00:13:20
  • kNN on Advertisement Dataset 5.5 00:05:12
  • Customer Churn using kNN 5.6 00:08:52
  • Customer Churn using kNN 5.7 00:07:04
  • Customer Churn using kNN 5.8 00:08:58
  • kNN on Cancer Dataset 5.9 00:10:50
  • kNN on Cancer Dataset 5.10 00:08:41
  • kMeans 6.1 00:03:18
  • kMeans 6.2 00:05:45
  • kMeans 6.3 00:03:42
  • kMeans 6.4 00:04:24
  • kMeans 6.5 00:06:30
  • kMeans Customer Segementation Case 6.6 00:06:59
  • kMeans Customer Segementation Case 6.7 00:09:44
  • kMeans Customer Segementation Case 6.8 00:14:30
  • Artificial Neural Network 7.1 00:01:52
  • Artificial Neural Network 7.2 00:07:35
  • Artificial Neural Network 7.3 00:06:14
  • Artificial Neural Network 7.4 00:02:04
  • Artificial Neural Network 7.5 00:03:59
  • Artificial Neural Network 7.6 00:06:41
  • Artificial Neural Network 7.7 00:06:42
  • Artificial Neural Network 7.8 00:03:32
  • Artificial Neural Network 7.9 00:08:28
  • Artificial Neural Network 7.10 00:15:31
  • Artificial Neural Network 7.11 00:11:24
  • Artificial Neural Network 7.12 00:08:17
  • Artificial Neural Network 7.13 00:05:57
  • Bank Customer Churn Case Using ANN 7.14 00:10:48
  • Bank Customer Churn Case Using ANN 7.15 00:12:26
  • Bank Customer Churn Case Using ANN 7.16 00:08:39
  • Bank Customer Churn Case Using ANN 7.17 00:13:42
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Includes:
  • Self-Paced Training
  • 12:51:11 Hours Videos
  • 97 Lessons
  • Certification Course
  • All Levels