Chevron Left
Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4.8
4,457개의 평가
838개의 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 807개 리뷰 중 76~100

교육 기관: Sundar J D

Feb 07, 2016

Great course and great instructor. Course covers regression models in great detail. The instructor's explanation of concepts and intuition behind why things are the way they are was really helpful to learn and appreciate the concepts.

교육 기관: YEH T P

Mar 11, 2017

This is an amazing tour about regression and machine learning. You will learn basic linear regression first and dig into some practical problem include overfitting, feature selection, cross validation, this is a great course for people who interested in machine learning and have basic programming skill.

교육 기관: Carlos D M

Jan 18, 2016

The topics are presented in a meaningful and understandable way. With enough detail, clarity, and fun. The instructors are super sweet and their dynamics in front of the camera are very inspiring.

The assignments are amazingly well designed. I get to practice the theory I learn from the lectures which truly reinforces what we review.

Even though I don't use the alternative tools (like Pandas), I appreciate that the organizers of the class prepare files and data sets for people who use those tools.

Another thing that's really valuable to me its' the fact that assignments (data, instructions, Jupyter, etc.) can be worked on completely offline and only need Internet connectivity to post results. Because all we do is enter numbers and select a few options, I have successfully submitted my assignments 10 minutes away from boarding a plane. I have had the chance work while riding a car (not driving it, LOL) or in an airplane. Because I have a full-time job, this is a HUGE advantage.

교육 기관: Fabricio N

Mar 27, 2016

Best course in data science out there.

교육 기관: Tobi L

Jan 12, 2016

There was way more interesting mathematics to linear regression than I ever imagined. I thought this was going to be a boring review of linear algebra and quadratic polynomials. I have never been so happy to be wrong!

교육 기관: Carlos L C

Jan 19, 2016

I learned a lot! Thanks

교육 기관: Sushil P B

Sep 08, 2016

Well organised. In depth optional lectures help you learn more about the theoretical foundations. Recommended.

교육 기관: Jarun N

Dec 23, 2015

concise and practical

교육 기관: Renato R S

Feb 19, 2016

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

교육 기관: Deleted A

Nov 21, 2016

Excellent course. Concepts are explained clearly and the exercises reinforce understanding.

교육 기관: Gaurav K J

Apr 22, 2018

I want to

교육 기관: Alejandro G L

Jan 17, 2016

It is an excellent course.

교육 기관: Jaiyam S

Jan 01, 2016

This is one of the best online courses out there and not just about Machine Learning. The course was very well organized and the teaching staff was very helpful in resolving whatever issues cropped up. I would suggest you to provide additional readings/ references at the end of the course in 'Closing remarks'. Thank you Profs. Emily and Carlos for the wonderful course. Keep up the good work! I am looking forward to the next one.

교육 기관: Aarshay J

Mar 09, 2016

A very good starting to the journey to Machine Learning. Just one disappointment, I was expecting the classification and clustering courses to start together but the specialization has been delayed by a long time now.

교육 기관: Paul C

Aug 13, 2016

This Machine Learning class and the rest of the Machine Learning series from the University of Washington is the best material on the subject matter. What really sets this course and series apart is the case-base methodology as well as in-depth technical subject matter. Specifically, the step through coding of the algorithms provides key insight that is seriously missed in other classes even in traditional academic settings. I highly encourage the authors and other Coursera publishers to continue to publish more educational material in the same framework.

교육 기관: Ahmed M M

Sep 07, 2016

awesome course, really i loved it

교육 기관: Thuong D H

Mar 05, 2016

Good course

교육 기관: Veer A S

Mar 21, 2018

Excellent course to learn about Regression models.

교육 기관: Carlos F A

Jun 09, 2016

I already knew how to do linear regression before taking this course; however, I had always struggled to understand how ridge and lasso regression worked and what their usefulness was; thanks to this course I was finally able to understand those concepts very well. The visual explanation of how the ridge and lasso regression work made this course well worth its time.

교육 기관: Gabor S

Jan 17, 2017

This a well thought out course. From the simple concepts it gradually takes you to the more complex ones. The quizzes and programming assignments help you to really understand the problems that were introduced in the videos. The video slides of every module can be downloaded as a pdf document which makes the material easily searchable. And last but not least Emily Fox is a great instructor.

교육 기관: Nilesh K

Jul 18, 2016

very good coverage of a wide range of topics. impressive!

교육 기관: Matthias B

Jan 03, 2016

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

교육 기관: Roopam K

Jul 26, 2017

excellent course. good pace and good assignments

교육 기관: Leonardo J T G

Aug 23, 2016

The best !!

교육 기관: Ziyue Z

Aug 10, 2016

Great course! Excellent overview of the goal of regression, and the difference between L1 and L2 regularization, as well as some generally applicable machine learning concepts/algorithms. Packed with material and very worthwhile.