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.
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About this Course
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Try Coursera for BusinessSkills you will gain
- Regression Analysis
- Supervised Learning
- Linear Regression
- Ridge Regression
- Machine Learning (ML) Algorithms
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Syllabus - What you will learn from this course
Introduction to Supervised Machine Learning and Linear Regression
Data Splits and Polynomial Regression
Cross Validation
Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net
Reviews
- 5 stars77.44%
- 4 stars16.84%
- 3 stars3.80%
- 2 stars0.81%
- 1 star1.08%
TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: REGRESSION
Well structured course. Concepts are explained clearly with hands on exercises.
This course is very helpful. The wonderfull part in this course was the final course project in which I had to create my own linear regression model by adding polynimial features and regularization.
It was a great learning experience with in-depth knowledge and practice-based demos helped me to understand the concepts easily.
Learned really about supervised learning and more importantly regularization and some available methods.
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