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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 226~250

교육 기관: Shikhar V

Mar 07, 2016

Very nice course! Every concept is explained in detail with proper illustrations.

교육 기관: songwei

Jan 15, 2016

cool, really great class about Regression model

교육 기관: LIU Y

Mar 22, 2016

best of the best, theoretically and practically

교육 기관: jun l

Mar 08, 2016

the course is great!

교육 기관: Dennis S

Aug 24, 2017

Thank you for this great course! :)

교육 기관: George P

May 16, 2017

Straight to the point and with useful material to check back whenever you feel is necessary. Learning but also good annotated notes in order to revise things later are very important.

교육 기관: Steve B

Mar 05, 2017

This is one of the hardest courses I've ever taken. The theory part reminded me of Differential Equations, which got rid of about half of my electrical engineering class. Then they ask you to program it in Python!! There's a lot of great theory and deep explanations and hands-on coding.

교육 기관: Nihal T

Sep 25, 2017

Great course to get started in the Machine learning , it covers each and every concept of Regression . All the concepts are explained in so simple way that even a high school kid wont have any trouble understanding Machine learning . I would highly recommend taking this.

교육 기관: Arthur Z

Feb 24, 2017

Top notch material presented in a really enjoyable manner. The professors are great.

교육 기관: Muhammad U C

Feb 12, 2016

Excellent. This is an ideal course in order to understand various aspects of regression techniques. Explanation using hands-on exercises helps me learn this course very effectively. I must appreciate the efforts of both Instructors (Prof. Emily & Prof. Carlos).

교육 기관: michal b

Jan 01, 2016

I took and finished Andrew Ng ML course before and I though I 'now i know something about ML', after finishing this course I feel less confident and I can see how many things there are ahead to learn. Especially when it comes to relation between size of sets vs features / model / tuning parameters of model. How much different prediction you can get with the same data!

I can't wait to next part because after Andres Ng's course I started mini project using classification.

교육 기관: Aditya K

Aug 15, 2016

rigorously explained some of the most important algorithms in regression world, also the pros and cons of using certain algorithm for certain conditions. totally worth

교육 기관: Tan W J I

Dec 04, 2016

Awesome! This one contained so much more information than I expected

교육 기관: Yu I

Aug 04, 2016

This course was super exciting! The explanation was very intuitive, using nice visualizations. The programming assignments was really practical. It would be great for machine learning newbees to learn regression.

교육 기관: Hemant V G

Mar 14, 2016

Course has covered regression in sufficient details and gave practical aspect of it. Thanks to Emily for very good content and teaching

교육 기관: Med N

Jan 11, 2016

One of the best courses I have taken!

Very good balance of depth and breadth of the material.

교육 기관: Muhammad A

Apr 01, 2017

Absolute loved the course. Highly recommended.

교육 기관: Aparajita K

Jun 14, 2016

All the mathematical details are very precisely and very well explained starting from basics.

교육 기관: Pradeep M

Jan 09, 2017

Simply brilliant

교육 기관: Ade I

May 07, 2016

A very well taught course.

Also very good instructors.

I really enjoyed this course.

교육 기관: Birbal

Oct 13, 2016

good

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.