Chevron Left
Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4,286개의 평가
814개의 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


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:

Machine Learning: Regression의 784개 리뷰 중 226~250

교육 기관: Keng-Hui W

Aug 18, 2016

I'll definitely keep learning the next course.

Some people criticized about graphlab (I thought they should offer 2 versions like RStudio instead a limit-free one. Although I feel comfortable when using graphlab, I'll still use scikit-learn after finishing all courses because it is free and I just use for personally.) but you can use scikit-learn to pass this course (although you have to spend more time) , so this is not a sufficient reason to not giving 5 stars for me.

Great course.

교육 기관: Craig B

Nov 29, 2016

A well thought out and nicely paced introduction to Regression following on from the equally good foundation course. I particularly like the way that the assignments assume an improving knowledge and familiarity with Python as the course progresses. It will be interesting to see if the subsequent courses in the specialisation continue in this vein - I hope so. I note the concerns that some have expressed about the use of graphlab.create for examples and assignments, but tend to think there is benefit from gaining familiarity with a number of different data science ML tools and libraries. Also additional code and instructions are available for those determined to use other tools such as Pandas and Scikit Learn.

교육 기관: Prashant R

Aug 08, 2016

This course is one the most brilliant courses available on machine learning. My only advise is to stick with the course even in the face of steep learning curve on some of the advanced machine learning techniques . Furthermore, completing the project using sklearn and python is bit difficult but very useful in long run.

교육 기관: Amar P R

Oct 14, 2016

very good quality course. Some more stress should be given to theoretical quizzes.

교육 기관: Omar N T

Mar 30, 2016

it gave more details than my class room. it also adopts a practical approach to learn. it is a great course in regression especially for practitioners.

Thanks Carlos and Emily :)

교육 기관: Rama K R N R G

Aug 19, 2017

I really liked the progression of the topics and coverage. Good presentation with good amount of details/depth in each topic.

교육 기관: Oshan M

Jun 23, 2017

thorough explanation. they cover most of the topics. lessons on ridge and lasso regression are great. would recommend for anyone looking to get into data science/ machine learning.

교육 기관: Kaixiang Y

Jun 27, 2017

Very good instructors

교육 기관: MANOJ K

Feb 08, 2016

Wow.... to complete this course, one really needs to work hard... one of the best teachers and the way they build concepts, so easy and systematic... thanks you so much for making me learn some of the challenging concepts with ease...

교육 기관: Lennart B

Feb 07, 2016

Thorough introduction to regression, the assignments are demanding, and the teachers very engaging. It would be nice if a wider range of applications and examples were presented.

교육 기관: 汪彦龙

Jan 19, 2016

The course is awesome!

교육 기관: Dhananjay M

Feb 08, 2016

It is an amazing course being taught by professor Emily . Being a computer science major it is very difficult to see how the statistical and mathematica algorithm we learn will be used. This course has helped me picturize the algorithm and with this case-study based approach it has helped me understand Regression really well.

교육 기관: Andre J

Mar 18, 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

교육 기관: Rubén S F

Feb 07, 2016

Great course which covers most of regression topics and important thigns such as lasso regression or ridge regression.

교육 기관: roshan s

Jan 06, 2016

Excellent course

교육 기관: Michael B

Feb 29, 2016

Excellent course on the use of regression in machine learning. It does not simply stop with simple linear regression but also tackles ridge and lasso regression using Python notebooks. One big advantage for those not familiar with Python is that the Python notebooks have just enough boiler plate code to make it feasible for Python beginners but not so much that the challenge is gone. The lectures can feel rather technical at times but this, at least in my mind, enhances the course and at no point did I feel I was "drowning" in formulas.

교육 기관: Sam S

Nov 23, 2016

Good pace, thorough presentation, challenging but reasonable programming exercises.

교육 기관: Qinzhe Z

Feb 18, 2017

Really can use in solving industry problem

교육 기관: Pranas B

Mar 19, 2016

Amazing course with good balance of visual material, practice, and optional math. Thanks Emily and Carlos, you are great teachers!

교육 기관: Wei F

Mar 07, 2016

I like this course a lot. Frankly speaking, this is my first completed course on Coursera. The instructor is so good that I could easily follow everything in the class. Give lots of credits to the assignments. They're very easy to follow for me. I really enjoy working on them. Thanks a lot!!!

교육 기관: Ruan P R T

Apr 30, 2016

All concepts are explained really well! Knowing all the mathematics behind machine learning can never hurt, but when it comes down to actually implementing something useful it all boils down to the practicalities of the implementation.

교육 기관: Yinan W

May 03, 2016

A very good course. Glad all the assignments are also compatible with pandas and scikit-learn

교육 기관: Amlan D

Mar 25, 2016

Nice intro to regression! Shorter lectures and more programming challenges would have made it even better.

교육 기관: Christopher W

Mar 28, 2016

Pretty challenging from a mathematical perspective, but extremely interesting and well-explained. I was glad to see there were plenty of opportunities to use Pandas and other Python libraries instead of just relying on Graphlab. Very happy with this class.

교육 기관: Bernardo N

Jan 16, 2016

Best Regression MOCC available online! Also consider the whole Machine Learning specialization, one of the best series you can find on this subject