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Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4.8
4,471개의 평가
839개의 리뷰

강좌 소개

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....

최상위 리뷰

PD

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!

CM

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:

Machine Learning: Regression의 808개 리뷰 중 801~808

교육 기관: Adrien L

Feb 02, 2017

No good without the missing course and capstone projects

교육 기관: Omar A C T

May 30, 2016

this was a really boring course not for the contet bu the teacher i fell bored every video because the theacher was really slow in everything tha she was showing, it is realy dificult to get focussed in the real topics when the teacher spend a lot of time explaining things at the end wont be evaluated. As an example I am not english native speaker but a had to put the playback speed to 1.50x in order to not get bored in all videos, it was really dificult to follow the teacher at the normal velocity , i just got sleep every video. and as a record i really like this topic so it is the tacher, I took the first course and it was a good experience but this one is owfull

교육 기관: Ignacio A d l T P

Feb 27, 2018

PLEASE REVIEW EVERY QUIZ, in several of them I had to input a different answer from what I thought was the correct answer after VERY carefully following instructions, reading and re-reading, executing, looking for alternatives, incorrectly graded quiz answers significantly have slowed me and tested my willingness to continue. If the quizzes need to grow to 14-20 questions so that the exercises become more "step by step" that would be OK, since the whole purpose of taking this for someone with 10-12 years of professional experience is to become confident that I have understood the concepts, when I have to guess responses my confidence on my understanding of the concepts gets strongly tested. I chose your specialization because it is project oriented, has use cases and breaks down every course into very detailed concepts, it is awesome to have been able to deepen my understanding of regression through this course but it could have taken me a fourth of the time and have been an achievement and something fun to work on if the quizzes were correct versus a chore and a source of stress.

If you need further information please reach me at

교육 기관: Andreas

Jan 04, 2017

This specialization is delayed for months now - very annoying! Don't give them money!

교육 기관: Konstantin K

Jun 19, 2016

I was not aible to complete this course for free. That was very disappointing! Universities like Stanford and John Hopkins find the opportunity to offer similar courses free of charge to peoople who want to learn. From University of Washington I have expected the same. Your bad!

Best regards

Konstantin

교육 기관: Eugene K

Feb 10, 2017

If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework. Despite the good lectures, the assignments don't help you develop the knowledge required for ML developer role.

Taking in consideration the permanent postponing the courses delivery, from summer 2016 to summer 2017, finally the most interesting part of the specialization was cancelled. I'm completely disappointed with the specialization learning expirience.

교육 기관: Monika K

May 03, 2016

I've spent a bit of time going through the Specialisation (paid for one course here) and other courses online that offer Machine Learning with Python. I looked at books too. I've come to the conclusion that it's unforgivable to teach it using graphlab (that you have to pay for after free licence expiry) when everyone else teaches scikit learn (sklearn) for good reason.The tools used on this course are also not very good.

Everyone else teaches using text editors - for a good reason, you learn how to code properly.

The lessons are also dry and there are far too many of them.

교육 기관: adam h

Mar 09, 2016

gets way too in-depth with the math behind regression, to the point that it deters from the learning process. was hoping to learn better methods of interpreting or enacting regression, not the inner workings of the algorithms.

assignments got overly complex with confusing instructions. there are definitely some leaps made in the assumptions of what students' python capabilities are. vague instructions caused more frustration than desire to continue learning.

will continue in the specialization, but will not hesitate to drop out if instruction continues like this.

very disappointed.