Course Summary
This course covers the basics of supervised learning and regression techniques, including linear and polynomial regression, regularization, and model selection. You'll also learn how to apply these techniques to real-world problems and evaluate the performance of your models.Key Learning Points
- Learn the fundamentals of supervised learning and regression techniques
- Apply regression techniques to real-world problems
- Evaluate the performance of your models
Job Positions & Salaries of people who have taken this course might have
- USA: $60,000 - $100,000
- India: ₹500,000 - ₹1,000,000
- Spain: €35,000 - €50,000
- USA: $60,000 - $100,000
- India: ₹500,000 - ₹1,000,000
- Spain: €35,000 - €50,000
- USA: $100,000 - $160,000
- India: ₹1,000,000 - ₹2,000,000
- Spain: €50,000 - €70,000
- USA: $60,000 - $100,000
- India: ₹500,000 - ₹1,000,000
- Spain: €35,000 - €50,000
- USA: $100,000 - $160,000
- India: ₹1,000,000 - ₹2,000,000
- Spain: €50,000 - €70,000
- USA: $120,000 - $190,000
- India: ₹1,500,000 - ₹3,000,000
- Spain: €60,000 - €90,000
Related Topics for further study
Learning Outcomes
- Understand the basics of supervised learning and regression techniques
- Apply regression techniques to real-world problems
- Evaluate the performance of your models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming in Python
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video Lectures
Similar Courses
- Machine Learning
- Applied Data Science with Python
- Introduction to Data Science in 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 basics of supervised learning for regression and understand its significance in data science. This course has received positive reviews from many users, who found it to be comprehensive and easy to follow.Key Aspect Users Liked About This Course
The course provides a solid foundation in the fundamentals of supervised learning for regression, making it ideal for beginners.Pros from User Reviews
- The course content is well-structured and easy to understand.
- The instructor is knowledgeable and engaging, making the course enjoyable.
- The course provides ample opportunities for hands-on practice.
- The course is suitable for beginners as well as those with some experience in data science.
- Many users found the course to be a great value for money.
Cons from User Reviews
- Some users felt that the course was too basic and lacked depth.
- A few users found the course to be too slow-paced.
- Some users experienced technical issues with the platform.
- The course may not be suitable for advanced learners looking for more advanced concepts.
- The course could benefit from more real-world examples and case studies.
Mark J Grover
4.8 Raiting
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