Supervised Machine Learning: Regression
This course ranks 9th in the top best online Machine Learning courses. This course exposes you to one of the most used supervised Machine Learning modeling families: regression. You'll learn how to utilize error metrics to compare models and how to train regression models to predict continuous outcomes. Best practices, such as train and test splits, and regularization strategies, are also covered in this course.
You should be able to do the following by the conclusion of this course:
- In the domain of supervised machine learning, distinguish between the uses and applications of classification and regression.
- Explain and use linear regression models.
- Compare and choose a linear regression model that best fits your data using a number of error measures.
- Explain why regularization may aid in the prevention of overfitting.
- Regressions using regularization: Elastic net, LASSO, and Ridge
Explain why regularization may aid in the prevention of overfitting. Regularization regressions like as Ridge, LASSO, and Elastic net can be used. This course is designed for aspiring data scientists who want to learn how to use Supervised Machine Learning Regression methods in a commercial scenario. You should be familiar with Python programming and have a basic grasp of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics in order to get the most out of this course.
Syllabus:
- WEEK1 (2 hours to complete): Introduction to Supervised Machine Learning and Linear Regression
- WEEK 2 (4 hours to complete): Data Splits and Cross Validation
- WEEK 3 (5 hours to complete): Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
Provider: Coursera
Cost: Premium
Rate: 4.7/5
Enroll here: https://tinyurl.com/9k8fzzbv
