Features and Polynomial Regression

Loading...
Ver programa

Destrezas que aprenderás

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

Revisiones

4.9 (125,366 calificaciones)
  • 5 stars
    116,051 ratings
  • 4 stars
    8,579 ratings
  • 3 stars
    547 ratings
  • 2 stars
    90 ratings
  • 1 star
    99 ratings
ML

Aug 19, 2017

Very helpful and easy to learn. The quiz and programming assignments are well designed and very useful. Thank Prof. Andrew Ng and coursera and the ones who share their problems and ideas in the forum.

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.

De la lección
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.

Impartido por:

  • 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

Explora nuestro catálogo

Inscríbete de manera gratuita y obtén recomendaciones personalizadas, actualizaciones y ofertas.