Chevron Left
返回到 Machine Learning: Regression

学生对 University of Washington 提供的 Machine Learning: Regression 的评价和反馈

4.8
星
5,581 个评分

课程概述

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....

热门审阅

KM

May 4, 2020

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

EV

Jun 24, 2016

An in-depth overview of the regression techniques and models. I think it went as deep into the concepts as I wanted it to go. Being a developer I found it quite understandable, and useful.Keep it up!

筛选依据：

601 - Machine Learning: Regression 的 625 个评论（共 1,001 个）

创建者 林玮

•

Oct 24, 2016

这门课提供了一个新的角度，从而使我更深刻地理解了回归预测。

创建者 江智彬

•

Feb 27, 2016

The teachers are very funny !

创建者 Vijai K S

•

Dec 6, 2015

It is a very thorough course.

创建者 Nar N

•

Oct 30, 2024

It was a really good course.

创建者 Yash A

•

May 20, 2020

Simply the best course ever.

创建者 Ronnie C

•

May 21, 2019

Very intuitive explanations!

创建者 Deleted A

•

May 1, 2019

Great course, great material

创建者 Jorge C

•

Dec 17, 2017

Very good indepth expanation

创建者 Mithilesh K S

•

Oct 22, 2017

awesome approach of teaching

创建者 Dzmitry D

•

Jan 6, 2016

Thank you! It was fantastic!

创建者 Hanna L

•

Aug 11, 2019

Thanks for the great class!

创建者 JOSE R

•

Nov 18, 2017

Very well explained. Thanks

创建者 Oleksii R

•

Jun 4, 2016

Great course. Thanks a lot.

创建者 Jose A V T

•

Dec 18, 2015

A great course!!! Thanks...

创建者 Sachin J P

•

Oct 15, 2020

It was a very good course.

创建者 Feng G

•

Jun 28, 2018

Thank you very much Emily!

创建者 Serge K

•

Feb 4, 2018

Emily is a great lecturer!

创建者 Naveed A

•

Apr 18, 2016

Best Teachers, Best Course

创建者 Xie Z

•

Feb 18, 2016

Great course!!! Thank you.

创建者 Alejandro G L

•

Jan 17, 2016

It is an excellent course.

创建者 Chitrang T

•

Dec 30, 2015

This is a very good course

创建者 Manjunath B

•

Feb 25, 2020

Very good course content,

创建者 ZhuangBairong

•

Aug 5, 2016

Really an awesome course!

创建者 Trong T L

•

May 24, 2016

Great intro to regression

创建者 Pascal U E

•

Mar 31, 2016

Emilie is a great teacher