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Machine Learning: Regression, University of Washington

4.8
Оценки: 3,890
Рецензии: 754

Об этом курсе

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....

Лучшие рецензии

автор: 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!

автор: RS

Nov 30, 2016

Excelent course, I highly recommend for those who are willing to learn machine learning from the basis, this module (linear regression) covered very important parts that I used to struggle to learn

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Рецензии: 725

автор: Xue

Dec 08, 2018

Very well-organized and clear. Learned a lot about regression.

автор: Mohamed Ali Habib

Nov 27, 2018

This course is extremely awesome!

The instructors are really professional and straight to the point. The topics are explained clearly and the assignments are crucially useful because you get to implement the concepts and algorithms in hand. Actually, you can't find a thing that's not nice about this course, at least I couldn't ;)

Very recommended!

автор: Aftab Nawaz

Nov 22, 2018

Amaizing one.. You Rocks

автор: PRAVEEN REDDY UPPALA

Nov 22, 2018

Nice content with hands-on. Much recommended.

автор: Ganji Ramu

Nov 08, 2018

E

автор: Jim Jose James

Nov 01, 2018

Great course and well explained. Need to invest time if you want to rally get benefit out of the content covered.

автор: Sathiraju Eswar

Oct 31, 2018

It was great to take this course. Thanks to Carlos and Emily for their efforts. It's been a useful course and certainly worth my time.

автор: leonardo duarte

Oct 28, 2018

Excellent course, the professors made it very easy to learn quite powerful technics like gradient descend and coordinate descend. I always saw them like black-boxes, but now, thanks to this course I not only understand how they really work, but I learned how to apply them to real data. This course was simply awesome.

автор: George Gousios

Oct 10, 2018

The course provided many useful insights on Regression techniques, and provided in depth understanding of the task in hand

автор: Hiral Prajapati

Oct 09, 2018

I loved this course because of the detail understanding of the concepts. I was looking for a course which provide detail understanding of algorithms, and here I am. I am giving four stars for what has been given in detail, not five because I something is left ;) interpretation..