This post contains notes from the lectures of the Machine Learning course at Stanford University – CS229: Machine Learning by Andrew Ng
With this article we continue the series of posts containing the lecture notes from CS229 class of Machine Learning at Stanford University.
Topics covered in this lecture:
- Handling multiple features – Multivariate Linear Regression
- Gradient Descent for Multivariate Linear Regression
- Feature Scaling
- How Learning Rate affects Gradient Descent
- Feature Generation
- Polynomial Regression
Machine Learning
Machine Learning is a field of study concerned with building systems or programs which have the ability to learn without being explicitly programmed.
Machine learning systems take in huge amounts of data and learn patterns and labels from that, to basically predict information on never-seen-before data.
Multivariate Linear Regression
Machine-Learning-Multivariate-Linear-RegressionThese notes were taken from the Machine Learning class CS229 at Stanford University. Video lectures can also be found at Coursera.
