Machine Learning: Regression

Archived

课程

英语

字幕可用

30 时

(2 评论2)

你无法访问存档的讲座

来源

来自www.coursera.org

状况

自定进度
免费获取
收费证书

更多信息

6 序列
等级介绍
字幕在 Arabic

你无法访问存档的讲座

Their employees are learning daily with

Learn more

课程详情

教学大纲

Week 1 - Welcome
Regression is one of the most important and broadly used machine learning and statistics tools out there. It allows you to make predictions from data by learning the relationship between features of your data and some observed, continuous-valued response. Re...
Week 1 - Simple Linear Regression
Our course starts from the most basic regression model: Just fitting a line to data. This simple model for forming predictions from a single, univariate feature of the data is appropriately called "simple linear regression".
In this module, we describe the...
Week 2 - Multiple Regression
The next step in moving beyond simple linear regression is to consider "multiple regression" where multiple features of the data are used to form predictions.
More specifically, in this module, you will learn how to build models of more complex relationsh...
Week 3 - Assessing Performance
Having learned about linear regression models and algorithms for estimating the parameters of such models, you are now ready to assess how well your considered method should perform in predicting new data. You are also ready to select amongst possible models ...
Week 4 - Ridge Regression
You have examined how the performance of a model varies with increasing model complexity, and can describe the potential pitfall of complex models becoming overfit to the training data. In this module, you will explore a very simple, but extremely effective ...
Week 5 - Feature Selection & Lasso
A fundamental machine learning task is to select amongst a set of features to include in a model. In this module, you will explore this idea in the context of multiple regression, and describe how such feature selection is important for both interpretability ...
Week 6 - Nearest Neighbors & Kernel Regression
Up to this point, we have focused on methods that fit parametric functions---like polynomials and hyperplanes---to the entire dataset. In this module, we instead turn our attention to a class of "nonparametric" methods. These methods allow the complexity of ...
Week 6 - Closing Remarks
In the conclusion of the course, we will recap what we have covered. This represents both techniques specific to regression, as well as foundational machine learning concepts that will appear throughout the specialization. We also briefly discuss some import...