Course Summary
This course covers supervised machine learning with a focus on regression techniques. Topics include linear regression, polynomial regression, regularization methods, and more.Key Learning Points
- Learn the fundamentals of supervised machine learning
- Understand regression techniques and how to apply them
- Get hands-on experience with real-world datasets
Related Topics for further study
- Supervised Machine Learning
- Regression Techniques
- Real-world Datasets
- Linear Regression
- Polynomial Regression
Learning Outcomes
- Apply regression techniques to solve real-world problems
- Understand the theory behind linear and polynomial regression
- Use regularization methods to improve model performance
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video Lectures
- Hands-on Projects
Similar Courses
- Unsupervised Machine Learning: Clustering & Dimensionality Reduction
- Applied Data Science with Python
Related Education Paths
Related Books
Description
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.
Outline
- Introduction to Supervised Machine Learning and Linear Regression
- Welcome/Introduction Video
- Introduction to Supervised Machine Learning: What is Machine Learning?
- Introduction to Supervised Machine Learning: Types of Machine Learning
- Supervised Machine Learning for Interpretation and Prediction
- Regression and Classification Examples
- Introduction to Linear Regression
- Linear Regression Demo - Part1
- Linear Regression Demo - Part2
- Linear Regression Demo - Part3
- Course Prerequisites
- Linear Regression Demo (Activity)
- Summary/Review
- Check for Understanding
- Check for Understanding
- End of Module Quiz
- Data Splits and Cross Validation
- Training and Test Splits
- Training and Test Splits Lab - Part 1
- Training and Test Splits Lab - Part 2
- Training and Test Splits Lab - Part 3
- Training and Test Splits Lab - Part 4
- Cross Validation
- 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
- Polynomial Regression
- Training and Test Splits Demo
- Cross Validation Demo
- Summary/Review
- Check for Understanding
- Check for Understanding
- Check for Understanding
- End of Module Quiz
- Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
- Bias Variance Trade off
- Regularization and Model Selection
- Ridge Regression
- LASSO Regression
- Polynomial Features and Regularization Demo - Part 1
- Polynomial Features and Regularization Demo - Part 2
- Polynomial Features and Regularization Demo - Part 3
- Further details of regularization
- Details of Regularization - Part 1
- Details of Regularization - Part 2
- Details of Regularization - Part 3
- Polynomial Features and Regularization Demo
- Details of Regularization Demo
- Summary/Review
- Check for Understanding
- End of Module Quiz
Summary of User Reviews
Discover the world of Supervised Machine Learning Regression with Coursera. Users rave about the course's comprehensive coverage of regression concepts and techniques. Learn how to use regression models to make predictions and gain insights from data without needing a lot of prior knowledge in statistics or programming.Key Aspect Users Liked About This Course
Comprehensive coverage of regression concepts and techniquesPros from User Reviews
- Easy to follow and understand lessons
- Great examples and exercises to apply new knowledge
- Engaging and knowledgeable instructors
- Excellent peer review system for assignments
- Flexible learning schedule and access to course materials
Cons from User Reviews
- Some lectures can be a bit repetitive
- Not enough emphasis on coding and implementation
- Some quizzes can be too difficult or vague
- Limited interaction with instructors and classmates
- Not enough real-world applications and case studies
Mark J Grover
4.7 Raiting
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