Implement gradient descent in linear regression problems, construct and evaluate simple linear models, and use feature engineering to create more complex supervised machine learning models.
Implement gradient descent in linear regression problems, construct and evaluate simple linear models, and use feature engineering to create more complex supervised machine learning models. This course is part of the Supervised Learning path – complete this path to learn how to use and evaluate a range of supervised learning algorithms.
Requirements
Previous experience of using Python is required. Prior knowledge of machine learning is not necessary.