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: Thomas E

•May 12, 2016

A bit to easy to get through the exercises but otherwise very enlightening and inspirering.

교육 기관: Rajesh V

•Jan 30, 2017

This course has a very detailed explanation of regression and quizzes which evaluates your understanding of the material.

교육 기관: Matthew M

•Jan 05, 2016

This course is an ideal mixture of theory, practical application, and coding. I really enjoyed it.

교육 기관: Josiah N

•Sep 28, 2016

Nice explanation of concepts, and very helpful with getting started on the programming assignments. The algorithms are explained well in pseudo code, and the instructor does a good job at explaining why they work the way they do. The math is not very challenging, so I never felt frustrated.

I only wish there was not such an emphasis on Graphlab. Although they do allow you to use other methods to finish the assignments, it feels as though more attention is given to explaining how Graphlab works instead of standard, free python libraries. I understand that they're trying to push a product, but I don't want to pay for something I'll only be using for a few courses. More attention should be given to sklearn.

교육 기관: Rashi K

•Dec 28, 2015

This was a great course and had in depth insights on a single topic. Worth doing.

교육 기관: Vaibhav O

•Jan 03, 2017

Excellent course on Regression! Must do for some serious hands on in the field

교육 기관: Dong Z

•Jan 01, 2016

Best course! really!!!

교육 기관: Popovics L

•Dec 29, 2015

Harder than the previous course, but helps to understand machine learning regression in deep.

교육 기관: Igor P

•Feb 26, 2016

I liked pretty much all of the content.

The lectures are detailed.

The assignments helped me understand the techniques used in regression. The step by step approach is great.

What I dislike a bit is the promotion of proprietary and expensive Graphlab software.

교육 기관: Yang X

•Feb 14, 2016

Love this course! Love the flexibility of the course but if rigor is what you want, they offer mathematical rigor in optional lectures as well. Great lectures and well-designed assignments. Highly recommended

교육 기관: Moayyad A Y

•Apr 17, 2016

best regression course , full details .

교육 기관: Saheer K

•Sep 11, 2016

Execellent

교육 기관: Hongkun Z

•Feb 07, 2016

Best online course about regression.

교육 기관: Sergio D H

•Feb 06, 2016

One of the best MOOCs I've ever tried. Great course materials and incredibly talented instructors. I can't recommend it enough.

교육 기관: Stephane F

•Dec 31, 2015

Professor Fox is explaining the main algorithms (gradient / coordinate descent) in a clear and understandable way. Quite often, in blogs and reviews, Andrew Ng's course (at Stanford) is mentioned as the reference, to me it looks like these series of courses can match Ng's course on machine learning (using Octave). Being based on Python I would give the advantage to this course and recommend it.

교육 기관: Ingrid B

•Aug 12, 2016

Really excellent guided course. Well explained and very useful exercises. Highly recommend

교육 기관: Nelson M M R

•Jul 21, 2016

Thanks, Amaizing !!

교육 기관: 张明

•Dec 04, 2015

Very responsible teachers and practical classes content.You can not only learning the ML theory from scratch,but also learn to implement the algorithm using python by yourself.This is the best ML course I ever seen.

Thanks for the teachers' hard-work.You are great!

교육 기관: Brian B

•Jan 11, 2016

One of the top Coursera courses I've had the pleasure of taking!

The instructors do a great job of making the math understandable (although I am a graduate student in applied math, so the mathy parts of machine learning aren't new).

교육 기관: Abu M S K

•Apr 10, 2016

The instructors give beautiful explanations which make course fun and doable.

교육 기관: Dennis M

•Apr 25, 2016

This is a great course, pretty obvious that Emily 1) knows her stuff and 2) put a lot of work into this class to provide an a nice look at regression.

교육 기관: Shikhar V

•Mar 07, 2016

Very nice course! Every concept is explained in detail with proper illustrations.

교육 기관: songwei

•Jan 15, 2016

cool, really great class about Regression model

교육 기관: LIU Y

•Mar 22, 2016

best of the best, theoretically and practically

교육 기관: vineet j

•Jan 31, 2017

excellent course