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- Welcome/Introduction Video
- Introduction to Supervised Machine Learning - Types of Machine Learning (Part 1)
- Introduction to Supervised Machine Learning - Types of Machine Learning (Part 2)
- Supervised Machine Learning (Part 1)
- Supervised Machine Learning (Part 2)
- Regression and Classification Examples
- Introduction to Linear Regression (Part 1)
- Introduction to Linear Regression (Part 2)
- (Optional) Linear Regression Demo - Part1
- (Optional) Linear Regression Demo - Part2
- (Optional) Linear Regression Demo - Part3
Supervised Machine Learning: Regression
Quick Facts
| particular | details | |||
|---|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course and certificate fees
The course fees for Supervised Machine Learning: Regression is -
| Head | Amount |
| 1 month | Rs. 3,277 |
| 3 month | Rs. 6,554 |
| 6 month | Rs. 9,831 |
certificate availability
Yes
certificate providing authority
Coursera
The syllabus
Module 1: Introduction to Supervised Machine Learning and Linear Regression
Videos
Readings
- Course Prerequisites
- Summary/Review
Assignments
- Practice Quiz: Introduction to Supervised Machine Learning
- Practice Quiz: Linear Regression
- Module 1 Graded Quiz: Introduction to Supervised Machine Learning and Linear Regression
App items
- Demo Lab: Linear Regression
- Practice Lab: Linear Regression
Module 2: Data Splits and Polynomial Regression
Videos
- Training and Test Splits (Part 1)
- Training and Test Splits (Part 2)
- (Optional) Training and Test Splits Lab - Part 1
- (Optional) Training and Test Splits Lab - Part 2
- (Optional) Training and Test Splits Lab - Part 3
- (Optional) Training and Test Splits Lab - Part 4
- Polynomial Regression
Reading
- Summary/Review
Assignments
- Practice Quiz: Training and Test Splits
- Practice Quiz: Polynomial Regression•10 minutes
- Module 2 Graded Quiz: Data Splits and Polynomial Regression
App items
- Demo Lab: Training and Test Splits
- Practice Lab: Polynomial Regression
Module 3: Cross Validation
Videos
- Cross Validation - Part 1
- Cross Validation Demo - Part 1
- Cross Validation Demo - Part 2
- Cross Validation Demo - Part 3
- Cross Validation Demo - Part 4
- Cross Validation Demo - Part 5
Reading
- Summary/Review
Assignments
- Practice Quiz: Cross Validation
- Graded: Module 3 Quiz: Cross Validation
App items
- Demo Lab: Cross Validation
- Practice Lab: Cross Validation
Plugin
- Reading: K-Fold Cross-Validation
Module 4: Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net
Videos
- Bias Variance Trade off (Part 1)
- Bias Variance Trade off (Part 2)
- Regularization and Model Selection
- Ridge Regression
- Lasso Regression (Part 1)
- Lasso Regression (Part 2)
- Elastic Net
- Polynomial Features and Regularization Demo - Part 1
- Polynomial Features and Regularization Demo - Part 2
- Polynomial Features and Regularization Demo - Part 3
Reading
- Summary/Review
Assignments
- Practice Quiz: Regularization Techniques
- Practice Quiz: Polynomial Features and Regularization
- Module 4 Graded Quiz: Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net
App item
- Demo Lab: Polynomial Features and Regularization
Module 5: Regularization Details
Videos
- Further details of regularization - Part 1
- Further details of regularization - Part 2
- (Optional) Details of Regularization - Part 1
- (Optional) Details of Regularization - Part 2
- (Optional) Details of Regularization - Part 3
Reading
- Summary/Review
Assignments
- Practice Quiz: Details of Regularization
- Module 5 Graded Quiz: Regularization Details
App items
- Demo Lab: Details of Regularization
- Practice Lab: Regularization
Module 6: Final Project
Reading
- Project Scenario
Peer review
- Submit your Project and Review Others
App item
- Lab for Final Project
Instructors
Mr Mark J Grover
Digital Content Delivery Lead
IBM
Mr Miguel Maldonado
Machine Learning Curriculum Developer
IBM
Ms Svitlana Kramar
Data Science Content Developer
IBM
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