Machine Learning: Regression(으)로 돌아가기

4.8

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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: Alexander S

•Feb 07, 2016

great, learning curve increasing.

교육 기관: Arash A

•Oct 26, 2016

Practical course complete with great content, assignment, and everything else that you need.

교육 기관: RAHUL M B

•Jun 14, 2017

Best Machine Learning Course ever..

교육 기관: Shiva R

•Nov 20, 2016

Concepts explained in detailed

교육 기관: Miguel P

•Dec 02, 2015

I

교육 기관: Giorgi G

•Dec 24, 2015

Very good course. I'd love to be course mentor :)

교육 기관: Lex T

•Feb 23, 2016

Really great stuff guys!

교육 기관: SMRUTI R D

•Feb 15, 2016

A very detailed course on regression with real data examples and which exposes the student to actual coding of different functions, rather than using already available functions. I got a very satisfying learning experience.

교육 기관: Damian T

•Mar 07, 2016

Difficult but very informative, it builds on the introduction course.

교육 기관: Angel S

•Dec 25, 2015

Excellent course in the line of the specialization. Highly recommended.

교육 기관: Mantraraj D

•May 05, 2018

The course should move away from the default graphlab implementation to scikit-learn as the package is outdated and python 2 is about to go out of support

교육 기관: Amal G

•Sep 10, 2016

I felt that the course was detailed and contained significant in-depth study about regression techniques. The assignments were well designed, starting from a single step and eventually enabling the candidate to be able to write the complete methods on his own.

교육 기관: Christopher A

•Dec 17, 2015

Excellent. My favourite machine learning course since Andrew Ng's class. Thorough treatment. Took us from easier hand-holding to deep in the implementation details. Talked both about theoretical considerations as well as practical fine tuning. Would maybe liked to have seen a bit more talked about the problems with data that can affect model fit (multicollinearity / skew / etc) but time constraints don't allow it in an already excellently "meaty" course.

교육 기관: Nitish V

•Sep 25, 2017

The course is really good for people planning to step into machine learning field. Not so deep , but covers all the relevant topics. Thanks to instructor for making it look so easy.

교육 기관: Mark T

•Jan 28, 2016

More excellent material presented and explained incredibly well. Thanks!

교육 기관: Eduardo d J C R

•Jan 11, 2016

I really liked the optional lectures with deeper explanations.

교육 기관: Giovanni B

•Dec 25, 2015

I think this course is great, Emily and Carlos explain things so clearly and provide excellent material

교육 기관: Sandeep J

•Jan 09, 2016

Great Course. So many regression concepts elegantly put!!!

교육 기관: Ben K

•Feb 23, 2016

Lasso, l2 regularization, ridge regression, etc. - appropriate level of technical detail, first principles discussion, etc. means that a lot of good info was packed into this course.

교육 기관: Dauren B

•Dec 23, 2017

Good insight into regression models. You will dive into the details of implementations of Lasso and Ridge regularization techniques. The course is actually easy to grasp for graduates with technical background, never the less gives good knowledge.

교육 기관: Taylor N

•Jan 11, 2016

I really learned a lot about going above and beyond OLS.

교육 기관: Alfredo A M S

•Jun 27, 2016

Started a little slow, and it may seem repetitive if you see all videos from one week in one day, otherwise I feel it has a good pace.The content was interesting and well explained.

교육 기관: Razikh S

•Feb 24, 2016

Awesome Course! Much Love to Emily! She is a wonderful prof!

교육 기관: kazi n h

•Jan 13, 2016

One of awesome series of machine learning!

교육 기관: Kishaan J

•May 30, 2017

Talks about each and every nitty-gritty details of the different types of Regression algorithms that are in use today!