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Volver a Machine Learning: Regression

Machine Learning: Regression, Universidad de Washington

4,013 calificaciones
771 revisiones

Acerca de este Curso

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....

Principales revisiones

por 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!

por 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!

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741 revisiones

por Zhongkai Mi

Feb 12, 2019

It provided practical details the are not described to much in others' courses.

por Akash Bhadouria

Feb 11, 2019

Course should contain a project related to real life.

por Yamin Ahmad

Feb 10, 2019

Excellent course that is the second in this specialization. It goes beyond the Foundations course and delves further into utilizing machine learning with regression based methods. The course also uses Python. There is some requirement that you should have some degree of familiarity with programming, although you can pick up some skills in coding in Python even if you are not familiar with it (- I wasn't familiar with Python much, although I am familiar with other languages).

Overall, highly recommended.

por Ayswarya S

Feb 05, 2019

Well taught !!Could have been better if practical teaching was more !!I mean teaching via coding was more:)

por Christopher Manhave

Jan 26, 2019

Great course. You get to write the algorithms for OLS regressions, ridge regression, lasso regression, and for k-nearest neighbor models. The instruction even includes some optional graduate-level videos on with more detailed explanations of how more advanced algorithms for solving the regressions may be developed (eg, subgradients for lasso regression).

por Pavan Bhadraiah

Jan 21, 2019

Very good assignments.

por Francisco Javier

Jan 20, 2019

A great curse focused on understanding the mathematics of the algorithms, clearly explained and detailed. Contains "advanced" optional topics for further learning and forces you to program you own algorithms.

Do not forget to save up the results and functions programmed in previous sections, as they might be required later in the course.

por Amirhossein Sabzevari

Jan 13, 2019

Well, I think Carlos teaches way more enthusiastically and energetically than Emily! But I did enjoy my course on this specialization.

por Wayne Pacholl

Jan 09, 2019

Great concepts but material presented is very theoretical with minimal practical examples. As such it is easy to get lost unless you have advanced mathematics skills.

por Manuel Gil

Jan 01, 2019

Amazing course! Thoroughly enjoyed it, and really appreciated the level of detail in some of the theoretical concepts. Yet it also stayed within what's practically useful and had a good amount of hands-on implementation.