Supervised Machine Learning: Regression

IBM via Coursera

Intermediate

Online

6 Weeks

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Module 1: Introduction to Supervised Machine Learning and Linear Regression

Videos

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

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

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Supervised Machine Learning: Regression

Intermediate

Online

6 Weeks

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The syllabus

Module 1: Introduction to Supervised Machine Learning and Linear Regression

Videos

Readings

Assignments

App items

Module 2: Data Splits and Polynomial Regression

Videos

Reading

Assignments

App items

Module 3: Cross Validation

Videos

Reading

Assignments

App items

Plugin

Module 4: Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net

Videos

Reading

Assignments

App item

Module 5: Regularization Details

Videos

Reading

Assignments

App items

Module 6: Final Project

Reading

Peer review

App item

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