Machine Learning: Regression(으)로 돌아가기

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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: Abishek R

•Feb 29, 2016

Extremely well planned and prepared. Thank you for the course.

교육 기관: Ganesan P

•Jun 20, 2016

Very good course to get the foundations right. Emily has done an excellent job in explaining the material and she reinforces the concepts with examples. I strongly believe this course will provide the required skills to explore further topics in this area. Great Job and thanks to Coursera for providing us this platform.

교육 기관: Sanjeev B

•Jan 10, 2016

Great instructors! Wish the problem sets were tougher and required more deeper thinking and choice of techniques to apply.

교육 기관: vishnu v

•Jan 03, 2016

Great course on regression. Covers almost all aspects on how to build a regression model from scratch, also covers few advanced topics aswell.

교육 기관: qi l

•Aug 01, 2016

Lovely lectures with easy to follow up mathematics

교육 기관: Soumya

•Mar 05, 2016

a little more details considering the the cost of the module would be appreciatiated

교육 기관: Juan C A

•Jan 09, 2016

This is an excellent course! Emily Fox does an excellent job at explaining what could be a hard concept grasp. I am talking about convex optimization and the LASSO solution. I have taken graduate level classes in convex optimization and the math is high level and can be challenging. The animation Emily presents along with the geometric intuitive explanation drives the intuition home. Thank you Emily and Carlos for this class.

교육 기관: Badrinath J

•May 03, 2016

The best

교육 기관: Chengye Z

•Nov 28, 2016

It's a very helpful course. I really have leart a lot, by both watching video and programming.

교육 기관: Chitrang T

•Dec 30, 2015

This is a very good course

교육 기관: Zhihui X

•Feb 19, 2016

Great course!!! Thank you.

교육 기관: Gustavo K A

•Jan 08, 2016

I had the clear sense of actually learning and not just "copying & pasting" bits of code. The questions and problems are challenging enough to make you stop and think about you just learned.

교육 기관: Chen Y

•Feb 18, 2016

Insightful

교육 기관: Wafic E

•Nov 06, 2016

An amazing course. You can sense the effort put into the presentations and assignment work. Loving the specialization thus far.

교육 기관: Rahul J

•Apr 03, 2017

An extremely well designed course, I am an instructional designer myself, so adding weight to the words. Would have appreciated a few more assignments for the last week though.

교육 기관: shubham j

•Jan 14, 2017

Great Course

교육 기관: Volodymyr L

•Mar 06, 2016

Super!

교육 기관: Mark C H

•Jan 04, 2016

Emily did a great job and presented this course in a very clear manner. I'm in the specialization primarily for the applications of regression tools and not as much for the mathematical theory. But I have to admit, I found it very helpful when Emily went into the proofs and theory behind tools such as gradient descent. She did this in a in a straightforward manner and it ultimately helped me understand the applications better. Carlos and Emily's visual 'movie' of the Lasso convergence was also extremely helpful. I'm very much looking forward to the next course in the specialization.

교육 기관: Michael L

•Mar 30, 2016

Excellent course with lots of hands on !

The teacher is excellent and provide clear explanation.

교육 기관: Syed A u R

•Jan 11, 2016

Exceptional course!. Emily went into great details of the regression algorithms and its application. Thoroughly enjoyed it.

교육 기관: Scott B

•Apr 20, 2016

Very practical and applied, great teaching style, and fun.

교육 기관: Jhonatan J

•Feb 01, 2016

great course, I learned a lot. Videos, slides, quiz, programming exercises, a lot of fun.

교육 기관: juan f r s

•May 15, 2016

Excelent course and very well explained. Many thanks to both of you Emily and Carlos. All the best

교육 기관: Sagara P

•Jun 19, 2016

Actually implemented Gradient Descent, Ridge Regression, Lasso etc.!!! No other course out their teaches the stuff contained in this one. Each algorithm is first run using a library. I used Pandas and SciKit. Then, we implement it from scratch! So the level of knowledge you gain is compounded.

교육 기관: Shashank G

•May 20, 2018

Excellent course with good hands-on