Machine Learning: Regression(으)로 돌아가기

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814개의 리뷰

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....

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:

교육 기관: Isura N

•Dec 28, 2017

WOW I got it

교육 기관: Robert R

•Jan 05, 2016

Just enough detail for beginners to provide a deep understanding of the subject

교육 기관: Krisda L

•Jul 19, 2017

Great course. It covers a lot of regression techniques you should know.

교육 기관: Konstantinos P

•Feb 14, 2017

Generally, very organized and helpful course.

The video presentations are perfect!

교육 기관: Cuiqing L

•Jan 28, 2017

great!

교육 기관: Milan C

•Apr 10, 2017

Very nice course. The course gave me a good overview in how deep you can dive even with regression.

교육 기관: Sabyasachi D

•May 19, 2017

Loved it!

교육 기관: fan c

•Apr 25, 2017

useful and easy to understand. Teacher is very nice.

교육 기관: Douglas P

•Oct 29, 2016

Excelent course!

교육 기관: Frank Z

•May 21, 2018

Very good class!

교육 기관: Sean Q Z

•Dec 08, 2016

Very good course with well-designed slides and ipython notebook.

교육 기관: Samuel d Z

•Jun 27, 2017

Awesome. You need a little bit of experience but things are explained really well. So glad I took this course, I tried another one from another university, it was disastrous. It certainly helps when you know how to do programming as this takes a lot of time and can be frustrating if you are new at it. Still worth learning it this way. Would recommend to use the GraphLab and maybe later redo it with standard Python tools.

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: Fan D

•Jan 03, 2017

The regression is done very well. I love the tutorials especially, they are very clear with good test feedbacks on some of the latter week contents. If you want to get into machine learning, this is a very important part to help you with all the other parts.

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!