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:

• Gradient Descent Algorithm
• Gradient Descent Intuition
• How Learning Rate affects Gradient Descent
• Gradient Descent for Linear 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.

Parameter Learning

These notes were taken from the Machine Learning class CS229 at Stanford University. Video lectures can also be found at Coursera.

Supervised Machine Learning: Regression and Classification

Learn the fundamentals of machine learning with Andrew Ng in this updated 3-course Specialization by DeepLearning.AI and Stanford Online. Build and train models using Python, NumPy, and scikit-learn for real-world AI applications. Ideal for beginners.

Coursera