Chevron Left
Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4,331개의 평가
821개의 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


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!


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 791개 리뷰 중 226~250

교육 기관: Mathias L

Mar 15, 2016

Very complete course and easy to understand

교육 기관: clark.bourne

Apr 21, 2016

Professional, comprehensive, worth to learn

교육 기관: Prashant R

Aug 08, 2016

This course is one the most brilliant courses available on machine learning. My only advise is to stick with the course even in the face of steep learning curve on some of the advanced machine learning techniques . Furthermore, completing the project using sklearn and python is bit difficult but very useful in long run.

교육 기관: Amar P R

Oct 14, 2016

very good quality course. Some more stress should be given to theoretical quizzes.

교육 기관: Omar N T

Mar 30, 2016

it gave more details than my class room. it also adopts a practical approach to learn. it is a great course in regression especially for practitioners.

Thanks Carlos and Emily :)

교육 기관: Rama K R N R G

Aug 19, 2017

I really liked the progression of the topics and coverage. Good presentation with good amount of details/depth in each topic.

교육 기관: Oshan M

Jun 23, 2017

thorough explanation. they cover most of the topics. lessons on ridge and lasso regression are great. would recommend for anyone looking to get into data science/ machine learning.

교육 기관: Kaixiang Y

Jun 27, 2017

Very good instructors

교육 기관: MANOJ K

Feb 08, 2016

Wow.... to complete this course, one really needs to work hard... one of the best teachers and the way they build concepts, so easy and systematic... thanks you so much for making me learn some of the challenging concepts with ease...

교육 기관: Lennart B

Feb 07, 2016

Thorough introduction to regression, the assignments are demanding, and the teachers very engaging. It would be nice if a wider range of applications and examples were presented.

교육 기관: 汪彦龙

Jan 19, 2016

The course is awesome!

교육 기관: Dhananjay M

Feb 08, 2016

It is an amazing course being taught by professor Emily . Being a computer science major it is very difficult to see how the statistical and mathematica algorithm we learn will be used. This course has helped me picturize the algorithm and with this case-study based approach it has helped me understand Regression really well.

교육 기관: Andre J

Mar 18, 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

교육 기관: Nihar S

Apr 21, 2016

Great class that breaks down machine learning concepts into simple digestible pieces.

교육 기관: Harley J

Jul 18, 2017

Very solid course for understanding machine learning principles, including developing methodical approaches to solving data problems.

교육 기관: 李扬骏

Apr 11, 2016

Good for everybody!

교육 기관: Juncheng P

Mar 07, 2017

a very detailed courses on regression

교육 기관: ChristopherKing

Aug 02, 2016


교육 기관: Jorge S N

Feb 22, 2016

I liked very much the way this course is structured. Simple and complete. Very well done.

교육 기관: Kim K L

Jan 03, 2016

Great course ... learned a lot!

교육 기관: rahul

Dec 31, 2017

Awesome Course!!!!

교육 기관: Nguyen T V

Jan 17, 2016

It's very interesting and challenging course, especially at the end. Thank you for your knowledge!

교육 기관: Lionel T L

Apr 16, 2017

complete, explicite, rich code

교육 기관: Sarah W

Aug 09, 2017

Great course! Well paced, learned a lot! Topics are well-explained.

교육 기관: Chia-Sheng L

Jan 04, 2016

This course offer many aspects like graphic comparison or detail math explanation help us understand more easily what a model or method means. The teacher have great effort on material design.