Chevron Left
Volver a Machine Learning: Regression

Opiniones y comentarios de aprendices correspondientes a Machine Learning: Regression por parte de Universidad de Washington

5,480 calificaciones

Acerca del Curso

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python....

Principales reseñas


16 de mar. de 2016

I really enjoyed all the concepts and implementations I did along this course....except during the Lasso module. I found this module harder than the others but very interesting as well. Great course!


4 de may. de 2020

Excellent professor. Fundamentals and math are provided as well. Very good notebooks for the’s just that turicreate library that caused some issues, however the course deserves a 5/5

Filtrar por:

276 - 300 de 984 revisiones para Machine Learning: Regression

por Sergio D H

6 de feb. de 2016

One of the best MOOCs I've ever tried. Great course materials and incredibly talented instructors. I can't recommend it enough.

por Luciano S

7 de ago. de 2017

I learned a lot of new concepts in this course. It is important to dive deeper than just understing how to use a set of tools.

por Rama K R N R G

19 de ago. de 2017

I really liked the progression of the topics and coverage. Good presentation with good amount of details/depth in each topic.

por akashkr1498

28 de mar. de 2019

please take care while framing assignment and quize question it is very difficult to understand what exactly u want us to do

por Ji H K

13 de ago. de 2020

This is a great course to understand the knowledge and concept of regression and also there are very useful practical quiz.

por Evaldas B

28 de nov. de 2017

Very good and accurate course about regresion. Not just the basics but a lot of things you can use in real life chalenges.

por Syed A R

10 de ene. de 2016

Exceptional course!. Emily went into great details of the regression algorithms and its application. Thoroughly enjoyed it.

por George G

10 de oct. de 2018

The course provided many useful insights on Regression techniques, and provided in depth understanding of the task in hand


30 de jul. de 2016

A very good introduction to Machine Learning: Regression, covering the wide range of topics and explanations in lucid way.

por Sanjeev B

10 de ene. de 2016

Great instructors! Wish the problem sets were tougher and required more deeper thinking and choice of techniques to apply.

por Rajesh V

30 de ene. de 2017

This course has a very detailed explanation of regression and quizzes which evaluates your understanding of the material.

por Aaron

2 de may. de 2020

Good introduction to regression with many crucial concepts, very friendly to the new learner on machine learning domain.

por venkatpullela

26 de oct. de 2016

The course is really good. The quizzes and support is really bad as they slow you down and distract with useless issues.

por Renato R S

19 de feb. de 2016

A very well designed course. I would recommend to anyone with serious goals on learning regression and machine learning.

por Min K

14 de sep. de 2017

Thank you very much to Instructor "Emily and Carlos" for such an excellent and very informative course on regression :)

por abhay k

13 de sep. de 2019

What I was trying to get at my starting stage in ML for last 2 months, this course given in 2 weeks.

Thank you coursera

por Oscar J

16 de may. de 2019

Step by Step about Regression explained well and easy to understand. Mandatory course for every data science begginer.

por Kishaan J

30 de may. de 2017

Talks about each and every nitty-gritty details of the different types of Regression algorithms that are in use today!

por Ruben S

7 de feb. de 2016

Great course which covers most of regression topics and important thigns such as lasso regression or ridge regression.

por Matthias B

3 de ene. de 2016

Great Course, well structured and following a clear path. Would enjoy some more of the optional technical backgrounds!

por Barnett F

6 de sep. de 2016

Bingo course, I learned two years ago ,but I just know the concepts, do not know how to code it ,now this course,,,,,

por Bipin A

26 de jul. de 2020

I was very satisfied by the way the courses are taught. And the assignments are not boringly easy. Would recommend.

por Rahul M

27 de feb. de 2016

It is an awesome Course For Beginners. But I wanted it to be in R since it is more easier to implement things in R.

por Jonathan L

14 de ene. de 2016

Visualization of ridge regression and lasso solution path in week 5 is worth the cost of the entire specialization.

por Devasri L

10 de abr. de 2020

Very helpful course. I sincerely thank Coursera and University of Washington to provide this opportunity to learn.