Machine Learning: Regression(으)로 돌아가기

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813개의 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: Ayush K G

•Sep 25, 2017

This course is full of information about regression in very simple way.

교육 기관: JOSE R

•Nov 18, 2017

Very well explained. Thanks

교육 기관: Saeed M

•Sep 21, 2017

great!

교육 기관: Tural I

•Apr 29, 2017

Just Great

교육 기관: FW Y

•Aug 16, 2017

做中学

교육 기관: Melwin J

•Jul 30, 2017

The best course on regression I have attended so far !!! I really liked the way professor explained the concepts. Has resources on in-depth details as well.

교육 기관: Mohamed A M A E

•Oct 17, 2017

it is a good contant and i learn more information such as

Simple linear regression, Multiple regressionAssessing , performanceRidge , regressionFeature selection & LassoNearest , neighbor & kernel regression

교육 기관: Mohit K

•Apr 21, 2018

I found this course more useful as compared to the first one. I really like this. One suggestion here, I would like you to incorporate is that you must have given small project work at the end of this course. This course is more technical and it would be helpful if we do some live project.

교육 기관: Lalithmohan S

•Mar 06, 2018

Fantastic content, so much to gain from this

교육 기관: Ruchi S

•Nov 08, 2017

e

교육 기관: Christopher S

•Aug 03, 2017

Great course. Heavy on the math but could use a little more on the implementation side.

교육 기관: Le H N

•Jun 29, 2018

I gain many knowledge from this course . Excellent.

교육 기관: Dongliang Z

•Feb 05, 2018

very good course! I enjoyed it very much.

교육 기관: Shuang D

•Jun 11, 2018

excellent course on regression. great hands-on experience.

교육 기관: VITTE

•Jun 09, 2018

Great course.

교육 기관: Prashant M

•Sep 30, 2017

This was a very satisying course with the intensity and queries that challenge me and wish to learn more. I am quite excited to learn more with the new ML bug that has caught me! Liberating.

교육 기관: Jessie J S

•May 12, 2018

I love this course! It explains more about Regression itself and not just discussing on how to use libraries for it! Very intuitive and informative at the same time!

교육 기관: Ian F

•Jun 09, 2017

Great course - you'll become much more accustomed to Python if you aren't already (I'm an R convert) and really learn the principles behind regression analysis.

교육 기관: Weilin C

•Jan 27, 2018

very helpful

교육 기관: Alessandro B

•Sep 27, 2017

e

교육 기관: Hongzhi Z

•Feb 05, 2018

Very Vivid and learn a lots, best AI specialization in Washington university

교육 기관: Liu S

•Nov 07, 2017

easy to learn! assignments are just at the right level!

교육 기관: Do A T

•Oct 26, 2017

very useful

교육 기관: Dipankar N

•Dec 11, 2017

Great course on Regression. This will help build basic for upcoming modules. Emily teaches the concepts in a simple way. I liked the structure and coverage of Regression topic.

교육 기관: Phil B

•Jan 29, 2018

This was the deep dive into regression that I was looking for, learning how and why to implement the various different algorithms that are used without being tied to a specific software package. Some of the other reviews complain about the use of graphlab but really it has no impact on the value of the course, because you can literally write the functions from scratch yourself using standard python and Numpy. The use of graphlab is just to speed things up in some of the programming assignments. One or 2 of the quizzes had some incorrect values in the notebooks but a quick search of the forums showed the correct ones and the ability to reattempt the quizzes means it's not a big issue. Emily is an excellent lecturer and the constant use of graphical aids and annotations makes it very easy to follow even with some of the fairly advanced maths.