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Machine Learning: Regression(으)로 돌아가기

Machine Learning: Regression, 워싱턴 대학교

4.8
4,166개의 평가
794개의 리뷰

About this Course

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

대학: 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!

대학: 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:

765개의 리뷰

대학: Xi Chen

May 22, 2019

Very intuitive explanations!

대학: Vibhutesh Kumar Singh

May 20, 2019

This is indeed a good course. Covering even much more than I had previously expected. The instructions were quite clear to me and the programming assignments were quite interesting.

대학: Oscar Salgado

May 16, 2019

Step by Step about Regression explained well and easy to understand. Mandatory course for every data science begginer.

대학: Jafed Encinas

May 14, 2019

Able to concentrate and stay focused for periods of several hours, even when tasks are relatively mundane, and doesn't make mistakes. He has a high boredom threshold. Always assured and confident in demeanour and presentation of ideas without being aggressively over-confident. No absences without valid reason in 6 months. Reaches a decision rapidly after taking account of all likely outcomes and estimating the route most likely to bring success. The decisions almost always turn out to be good ones.

This Course always completes any assignment on time and to a high standard. This Course has outstanding artistic or craft skills, bringing creativity and originality to the task. Aiming for a top job in the organization. He sets very high standards, aware that this will bring attention and promotion. This Course pays great attention to detail. He always presented work properly checked and completely free of error.

대학: Dohyoung Chung

May 11, 2019

Thank you for a good lecture.

The material was excellent and explanation was quite detailed and easy to understand.

Some of the programming was a little bit tricky, but I was able to pull through.

Thank you again for your efforts and I am looking forward to seeing you in the next course

대학: Vansh Srivastava

May 10, 2019

nice

대학: Nikhil Pandey

May 01, 2019

Great course, great material

대학: MAO MAO

Apr 29, 2019

Very good for beginners

대학: Mukul kumar

Apr 22, 2019

excellent course . lots of interesting things i have learned

대학: Nipun Goel

Apr 21, 2019

Please get rid of SFrame and graphlab. However, professor is awesome!