학생용
Chevron Left
Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4.8
별점
5,264개의 평가
984개의 리뷰

강좌 소개

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....

최상위 리뷰

PD
2016년 3월 16일

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!

KM
2020년 5월 4일

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

필터링 기준:

Machine Learning: Regression의 951개 리뷰 중 276~300

교육 기관: Evaldas B

2017년 11월 28일

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

교육 기관: Adeel R

2016년 1월 10일

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

교육 기관: George G

2018년 10월 10일

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

교육 기관: LAVSEN D

2016년 7월 30일

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

교육 기관: Sanjeev B

2016년 1월 10일

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

교육 기관: Rajesh V

2017년 1월 30일

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

교육 기관: Aaron

2020년 5월 2일

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

교육 기관: venkatpullela

2016년 10월 26일

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

교육 기관: Renato R S

2016년 2월 19일

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

교육 기관: Min K

2017년 9월 14일

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

교육 기관: abhay k

2019년 9월 13일

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

교육 기관: Oscar J

2019년 5월 16일

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

교육 기관: Kishaan J

2017년 5월 30일

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

교육 기관: Rubén S F

2016년 2월 7일

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

교육 기관: Matthias B

2016년 1월 3일

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

교육 기관: Barnett F

2016년 9월 6일

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

교육 기관: Bipin A

2020년 7월 26일

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

교육 기관: Rahul M

2016년 2월 27일

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.

교육 기관: Jonathan L

2016년 1월 14일

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

교육 기관: Devasri L

2020년 4월 10일

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

교육 기관: Deepak K S

2016년 11월 18일

Great Course! Complex things explained in simple ways. Challenging Assignments helped in reinforcing the concepts.

교육 기관: Maxwell N M

2016년 4월 7일

Lasso is very cool for dimension reduction i discover another algorithm powerfull than Personal Component Analysis

교육 기관: Jim J J

2018년 11월 1일

Great course and well explained. Need to invest time if you want to rally get benefit out of the content covered.

교육 기관: Chokdee S

2017년 4월 15일

This is one of my favorite courses for ML, The best course for learning regression stuffs ever. I really love it.

교육 기관: kripa s

2019년 3월 25일

I must say it was great learning experiance. Everything releted to ML regression has been covered so eloquently.