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

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!

PD

Mar 16, 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!

筛选依据：

926 - Machine Learning: Regression 的 950 个评论（共 1,001 个）

创建者 mohinish n

•

Mar 22, 2018

Gives good abstraction of underlying algorithm.

创建者 Rushikesh M N

•

Nov 19, 2019

Detailed derivation, Loved the way they teach.

创建者 João S

•

Jan 7, 2016

Nioce course. Compreensive notes and nice (&fu

创建者 Andy L

•

Aug 4, 2017

Great course to get started with regression.

创建者 rajeev r

•

Jan 26, 2020

Nice introductory ML concepts to star with.

创建者 shaurya s

•

Jan 1, 2016

Excellent course except the last week :)

创建者 mohammed T

•

Mar 11, 2018

i wish that you have used scikit learn

创建者 Pier L L

•

Sep 20, 2016

Very good course. I really liked it.

创建者 Aman G

•

Sep 24, 2018

Don't bug me regarding the review.

创建者 gaozhipeng

•

Feb 12, 2016

Nice course! Thank you very much ~

创建者 Paul M

•

Dec 22, 2017

Excellent overview. Great slides

创建者 Michael L

•

Mar 18, 2017

Far too math, much less practice

创建者 Shashidhar Y

•

Feb 28, 2019

Good interactive courses.

创建者 egonigilist

•

Aug 17, 2017

several errors in exams

创建者 Jeyaprabu J

•

Mar 4, 2016

detailed but slower...

创建者 Gaurav S

•

Dec 30, 2015

Good and Insightful

创建者 Mehul P

•

Aug 9, 2017

Nicely explained.

创建者 Sandeep K S

•

Jan 25, 2016

excellent course

创建者 Qing W

•

Dec 5, 2017

actually good

创建者 James H

•

Nov 12, 2016

Great course

创建者 Abhishek m

•

Jan 23, 2021

nice course

创建者 PHILIPPE R

•

Jan 26, 2016

Nice course

创建者 NIGAM P

•

Nov 1, 2020

Great Job!

创建者 Rohit K S

•

Sep 30, 2020

Nice One!!

创建者 Bruno G E

•

Apr 17, 2016

Awesome!