Volver a Machine Learning: Regression

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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....

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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746 revisiones

por Akash Gupta

•Mar 09, 2019

regression best now

por Ayush Kumar

•Mar 08, 2019

Great in-depth coverage

por Shashidhar Yalagi

•Feb 28, 2019

Good interactive courses.

por Maryam Alsadat Andalib

•Feb 28, 2019

The design of the course and presentations are great. It was very useful for my career development and fun. But, I think that the material is outdated and need a major update, especially Python packages and codes. Also, the forums are not active anymore.

por Piyush Gupta

•Feb 25, 2019

The programming assignments were tough ! but the course covers the content very effectively..

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).

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