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Supervised Machine Learning: Regression
  • Level Professional
  • المدة 21 ساعات hours
  • الطبع بواسطة IBM
  • Offered by

عن

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning  Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

الوحدات

Course Introduction

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Welcome/Introduction Video

1 Readings

  • Course Prerequisites

Introduction to Supervised Machine Learning

    • 5 Videos
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Introduction to Supervised Machine Learning

5 Videos

  • 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

Linear Regression

    • 5 Videos
    • 2 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Linear Regression

2 External Tool

  • Demo Lab: Linear Regression
  • Practice Lab: Linear Regression

5 Videos

  • 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

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Module 1 Graded Quiz: Introduction to Supervised Machine Learning and Linear Regression

1 Readings

  • Summary/Review

Training and Test Splits

    • 6 Videos
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Training and Test Splits

1 External Tool

  • Demo Lab: Training and Test Splits

6 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

    • 1 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Polynomial Regression

1 External Tool

  • Practice Lab: Polynomial Regression

1 Videos

  • Polynomial Regression

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Module 2 Graded Quiz: Data Splits and Polynomial Regression

1 Readings

  • Summary/Review

Cross Validation

    • 6 Videos
    • 2 ExternalTool
    • 1 Plugin
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Cross Validation

2 External Tool

  • Demo Lab: Cross Validation
  • Practice Lab: Cross Validation

6 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

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Graded: Module 3 Quiz: Cross Validation

1 Readings

  • Summary/Review

Regularization Techniques

    • 7 Videos
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Regularization Techniques

7 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

    • 3 Videos
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Polynomial Features and Regularization

1 External Tool

  • Demo Lab: Polynomial Features and Regularization

3 Videos

  • Polynomial Features and Regularization Demo - Part 1
  • Polynomial Features and Regularization Demo - Part 2
  • Polynomial Features and Regularization Demo - Part 3

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

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

1 Readings

  • Summary/Review

Details of Regularization

    • 5 Videos
    • 2 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Details of Regularization

2 External Tool

  • Demo Lab: Details of Regularization
  • Practice Lab: Regularization

5 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

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 5 Graded Quiz: Regularization Details

1 Readings

  • Summary/Review

Honors Final Project

    • 1 Readings
    • 1 PeerReview
    • 1 ExternalTool
Show info about module content

1 External Tool

  • Lab for Final Project

1 Peer Review

  • Submit your Project and Review Others

1 Readings

  • Project Scenario

Auto Summary

Discover the art of predicting continuous outcomes with the **Supervised Machine Learning: Regression** course, designed for those eager to delve into the data science and AI realm. Guided by expert instructors on Coursera, this professional-level course offers a comprehensive exploration of regression techniques in supervised machine learning. Over the span of this course, learners will master the essentials of linear regression models and the application of various error metrics to evaluate and compare models effectively. You will also gain insights into best practices, including train-test splits and regularization methods such as Ridge, LASSO, and Elastic net, to prevent overfitting and enhance model performance. Ideal for aspiring data scientists aiming to elevate their expertise in a business context, this course requires a solid foundation in Python programming, alongside a fundamental grasp of data cleaning, exploratory data analysis, calculus, linear algebra, probability, and statistics. Choose between Starter and Professional subscription options to access this 1260-minute immersive learning experience and advance your skills in supervised machine learning regression techniques.

Mark J Grover
Instructors

Mark J Grover

Miguel Maldonado
Instructors

Miguel Maldonado

Svitlana (Lana) Kramar
Instructors

Svitlana (Lana) Kramar