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Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4.8
4,471개의 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 176~200

교육 기관: Aviad B

Oct 10, 2017

Excellent course. Highly recommended. Emily Fox is clear and comprehensive. In addition, this module's exercises can be fully completed using Python's Pandas sklearn and numpy libraries and without requiring the propriety GraphLab library. Good work!

교육 기관: Jonathan C

Jan 04, 2018

so much good information

교육 기관: Ayan M

Aug 08, 2016

Absolutely fantastic and pretty much enriching the knowledge. Thank you!

교육 기관: Tsz W K

Apr 25, 2017

It's a truely amazing course. Having studied so much econometrics from undergraduate to PhD, I still learnt so much from this regression course. This course teaches me regressions in a way that is very different from any economics/business schools I have ever attended. While it is technically less demanding than most econometric courses from second year (UG) onwards, it is the applied/practical nature of this course that makes it so valuable.

교육 기관: Sanjay A

Apr 11, 2017

Very good

교육 기관: yuanfan p

Jun 18, 2017

Concise. Hope for more content.

교육 기관: Nadya O

May 06, 2017

Great material, this was tougher than the previous course. It is challenging and more exercises to practice which help to a better understanding of the concepts. Great mentors!

교육 기관: Kapil K

Feb 14, 2017

its a great course. little bit disappointed from the decision of not continuing Recommended systems and capstone project. PLEASEEEEEE roll out course 5 and 6 as well

교육 기관: Ferenc F P

Jan 10, 2018

This is a very good introductory class to regression. Even though I had taken already other classes in regression, like Statistical inference or Machine learning from Stanford, this course provided me much better understanding about the variance and bias of a model, as well as, how the the true error and test error is related. For some Quiz the result is different with scikit-learn than with Graphlab while the Quiz is prepared for Graphlab results. What is really helping is the notebooks provided to each programming assignment, so basically one need to write only a few lines of code when using Graphlab in order to pass the Quiz. I spent much more time making programs from zero with scikit learn (due to different results I gave it up in the last 3 weeks and used only notebook with Graphlab). Learning the usage of Graphlab is not so difficult, so I had no problem with that.

교육 기관: Lyu Y

Oct 24, 2017

Great Courses

교육 기관: Olexandra Z

Feb 05, 2017

Really great explanations for complex and important principles as well as math approaches and tools. Being a mathematician, I thought that in this math aspect there would be nothing new for me, but still it was a great refreshment and very useful explanations to understand how those methods actually work for machine learning tasks. Great balance of theory and practical applications! Thank you!

교육 기관: Michele P

Aug 23, 2017

Very nice explanation of ridge and lasso regression. Assignments are easier than in Classification. I highly recommend this course!

교육 기관: Saheed S

Sep 19, 2017

Nice course. I started with this specialization as a beginner. I was very intuitive and great course I would recommend to others people interested in data science.

교육 기관: Alejandro T

Dec 04, 2016

Very thorough course and very approachable much better than course one of specialization

교육 기관: 邓松

Dec 06, 2016

very helpful

교육 기관: Mark W

Aug 12, 2017

Excellent course. Emily and Carlos are fantastic teachers and have clearly put in a huge amount of effort in makign a great course. Thanks guys!

교육 기관: Ahmed A

Nov 30, 2015

I was only able to complete week 1 to week 3 thoroughly, and random check on other weeks due to limited time at my disposal at this moment.

In general, I found the course to be very interesting and an excellent introduction to building predictive models . Particularly , i appreciate the way mathematical formulations was explained to carry along beginners in this areas.

Nonetheless, I would suggest that the general notation slide in week 2 should include concrete data example in a table to explain the notations ie. x[j], xi[j], etc

교육 기관: Thomas K A W

Jan 08, 2018

Great course! I love the instructors and the thoroughly designed structure of their course. The effort they put into this course certainly shines through every video!

교육 기관: WEI Y

Jul 05, 2018

Really great course! Highly recommend it!

교육 기관: Adil A

Mar 15, 2017

Very nice course... The instructors were really great, the explanations, the presentations, even the color schemes were all really great... Definitely one of the most fun courses I've taken at Coursera... The assignments were also well designed...

교육 기관: g

Aug 29, 2016

Intuitive and very helpful, great assignment not too hard

교육 기관: Jooho S

May 24, 2016

This course helped me a lot to understand regression. Now I can apply this idea to my own work.

교육 기관: xichen

Mar 08, 2016

Really cool and practical

교육 기관: nazar p

May 06, 2017

good stuff.

교육 기관: Nicolas T

Dec 18, 2015

Best Machine learning mooc !!!