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:

  • Machine Learning Model Representation
  • Introduction to Linear Regression
  • Cost Function: Definition and Intuition
  • Examples for Model and Cost Function
Go back: Introduction
Skip to next Lecture: Parameter Learning

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.

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Model and Cost Function

Machine-Learning-Model-and-Cost-Function

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
Supervised Machine Learning: Regression and Classification

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine ... Enroll for free.
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Go back: Introduction
Next Lecture: Parameter Learning