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Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4.8
4,464개의 평가
839개의 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

Mar 17, 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!

CM

Jan 27, 2016

I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!

필터링 기준:

Machine Learning: Regression의 808개 리뷰 중 51~75

교육 기관: Dmitri T

Apr 25, 2016

Great course and makes a solid practical foundation for regression. Really enjoyed it.

교육 기관: Venkata D

Dec 29, 2015

Great material, homeworks and teaching

교육 기관: ChangIk C

Oct 25, 2016

Learned a lot recommend!

교육 기관: Jorge C

Dec 17, 2017

Very good indepth expanation

교육 기관: Ali A

Mar 05, 2016

All what I can say is if there is ten stars I would have given them to this course. It is just amazing and very very very helpful.

교육 기관: Evaldas B

Nov 28, 2017

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: 海上机械师

Jul 17, 2016

Some practical skill and some theoretical knowledge are bought to me. I am so glad to enjoy them.

교육 기관: ZhuangBairong

Aug 05, 2016

Really an awesome course!

교육 기관: Abe E

Apr 28, 2017

Excellent. I used some of the videos to prepare and brush up for job interviews. Super helpful to play back at double speed ;-)

교육 기관: Mohammad A K

Mar 12, 2016

Very practicum course, probably one of the best MOOCs course for Regression. I am from CS background and honestly speaking, I have passed hard time to catch all the concept. Nonetheless, great instructor and 5 stars for her! I believe she left no stone upturned to make the course understand for us. Thank all.

교육 기관: Belal M

Oct 13, 2017

perfect course

교육 기관: Yihong C

Mar 31, 2016

wonderful explanation of regression

교육 기관: Johan M

Dec 22, 2015

Excellent, thorough course on regression! Thank you Emily and Carlos.

교육 기관: Oscar

Jun 12, 2017

Excellent specialization

교육 기관: Chencheng X

Feb 22, 2016

Really good course

교육 기관: Itrat R

Jan 23, 2017

Excellent Course!!!

교육 기관: Tamir Z

Mar 17, 2016

Great course

교육 기관: Eftychios V

Jun 25, 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!

교육 기관: James D

Apr 09, 2016

Great instructors make this course one of the best I have taken. Thank you very much!!

교육 기관: Michael H

Sep 02, 2016

Fantastic course. Perfect balance of practice and theory. I have tried learning regression a number of times now and after doing this course I feel like I finally have a good grasp on it. Absolutely no complaints.

교육 기관: Satyam C

Jun 27, 2018

ever best for regression. even better than Andrew NG. Detailed Mathematics explanation is part of this course

교육 기관: Serhiy

Feb 05, 2018

Emily is a great lecturer!

교육 기관: Jing

Aug 14, 2017

A little bit boring and hard to focus on, sometimes

교육 기관: Ayman K

Jan 19, 2017

I've studied regression and other ML concepts in so many ways, but never have I been able to understand the concepts as I did after auditing this course. I learned the following the hard way: If you want to really get an intuitive, theoretical & practical understanding of ML, you have to listen to a statistician! If I were to realize this fact earlier, I would've never jumped into ML without a degree in statistics. I do highly recommend this course.

교육 기관: Yaron K

Aug 14, 2016

Prof. Emily Fox is definitely enthusiastic, and gives clear explanations. The assignments add to the understanding of the material. While Graphlab, which is idiosyncratic is still used, explanations are given how to use Sci-Kit learn. A technical course, not only ideas - put also algorithms.