Multiple Features

Loading...
Stanford University
4.9 (111,214 ratings) | 2.5M Students Enrolled
View Syllabus

Skills You'll Learn

Logistic Regression, Artificial Neural Network, Machine Learning (ML) Algorithms, Machine Learning

Reviews

4.9 (111,214 ratings)
  • 5 stars
    103,044 ratings
  • 4 stars
    7,527 ratings
  • 3 stars
    474 ratings
  • 2 stars
    83 ratings
  • 1 star
    86 ratings
SK

Oct 26, 2017

Amazing course for people looking to understand few important aspects of machine learning in terms of linear algebra and how the algorithms work! Definitely will help me in my future modelling efforts

AD

Apr 22, 2017

Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.

From the lesson
Linear Regression with Multiple Variables
What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.

Taught By

  • Andrew Ng

    Andrew Ng

    CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.