课程信息

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学生职业成果

44%

完成这些课程后已开始新的职业生涯

43%

通过此课程获得实实在在的工作福利

18%

加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 2 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为18 小时
英语(English)
字幕:英语(English), 韩语, 阿拉伯语(Arabic)

您将获得的技能

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis

学生职业成果

44%

完成这些课程后已开始新的职业生涯

43%

通过此课程获得实实在在的工作福利

18%

加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 2 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为18 小时
英语(English)
字幕:英语(English), 韩语, 阿拉伯语(Arabic)

提供方

华盛顿大学 徽标

华盛顿大学

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up94%(17,688 个评分)Info
1

1

完成时间为 1 小时

Welcome

完成时间为 1 小时
5 个视频 (总计 20 分钟), 3 个阅读材料
5 个视频
Welcome!1分钟
What is the course about?3分钟
Outlining the first half of the course5分钟
Outlining the second half of the course5分钟
Assumed background4分钟
3 个阅读材料
Important Update regarding the Machine Learning Specialization10分钟
Slides presented in this module10分钟
Reading: Software tools you'll need10分钟
完成时间为 3 小时

Simple Linear Regression

完成时间为 3 小时
25 个视频 (总计 122 分钟), 5 个阅读材料, 2 个测验
25 个视频
Regression fundamentals: data & model8分钟
Regression fundamentals: the task2分钟
Regression ML block diagram4分钟
The simple linear regression model2分钟
The cost of using a given line6分钟
Using the fitted line6分钟
Interpreting the fitted line6分钟
Defining our least squares optimization objective3分钟
Finding maxima or minima analytically7分钟
Maximizing a 1d function: a worked example2分钟
Finding the max via hill climbing6分钟
Finding the min via hill descent3分钟
Choosing stepsize and convergence criteria6分钟
Gradients: derivatives in multiple dimensions5分钟
Gradient descent: multidimensional hill descent6分钟
Computing the gradient of RSS7分钟
Approach 1: closed-form solution5分钟
Approach 2: gradient descent7分钟
Comparing the approaches1分钟
Influence of high leverage points: exploring the data4分钟
Influence of high leverage points: removing Center City7分钟
Influence of high leverage points: removing high-end towns3分钟
Asymmetric cost functions3分钟
A brief recap1分钟
5 个阅读材料
Slides presented in this module10分钟
Optional reading: worked-out example for closed-form solution10分钟
Optional reading: worked-out example for gradient descent10分钟
Download notebooks to follow along10分钟
Fitting a simple linear regression model on housing data10分钟
2 个练习
Simple Linear Regression14分钟
Fitting a simple linear regression model on housing data8分钟
2

2

完成时间为 3 小时

Multiple Regression

完成时间为 3 小时
19 个视频 (总计 87 分钟), 5 个阅读材料, 3 个测验
19 个视频
Polynomial regression3分钟
Modeling seasonality8分钟
Where we see seasonality3分钟
Regression with general features of 1 input2分钟
Motivating the use of multiple inputs4分钟
Defining notation3分钟
Regression with features of multiple inputs3分钟
Interpreting the multiple regression fit7分钟
Rewriting the single observation model in vector notation6分钟
Rewriting the model for all observations in matrix notation4分钟
Computing the cost of a D-dimensional curve9分钟
Computing the gradient of RSS3分钟
Approach 1: closed-form solution3分钟
Discussing the closed-form solution4分钟
Approach 2: gradient descent2分钟
Feature-by-feature update9分钟
Algorithmic summary of gradient descent approach4分钟
A brief recap1分钟
5 个阅读材料
Slides presented in this module10分钟
Optional reading: review of matrix algebra10分钟
Exploring different multiple regression models for house price prediction10分钟
Numpy tutorial10分钟
Implementing gradient descent for multiple regression10分钟
3 个练习
Multiple Regression18分钟
Exploring different multiple regression models for house price prediction16分钟
Implementing gradient descent for multiple regression10分钟
3

3

完成时间为 2 小时

Assessing Performance

完成时间为 2 小时
14 个视频 (总计 93 分钟), 2 个阅读材料, 2 个测验
14 个视频
What do we mean by "loss"?4分钟
Training error: assessing loss on the training set7分钟
Generalization error: what we really want8分钟
Test error: what we can actually compute4分钟
Defining overfitting2分钟
Training/test split1分钟
Irreducible error and bias6分钟
Variance and the bias-variance tradeoff6分钟
Error vs. amount of data6分钟
Formally defining the 3 sources of error14分钟
Formally deriving why 3 sources of error20分钟
Training/validation/test split for model selection, fitting, and assessment7分钟
A brief recap1分钟
2 个阅读材料
Slides presented in this module10分钟
Polynomial Regression10分钟
2 个练习
Assessing Performance26分钟
Exploring the bias-variance tradeoff8分钟
4

4

完成时间为 3 小时

Ridge Regression

完成时间为 3 小时
16 个视频 (总计 85 分钟), 5 个阅读材料, 3 个测验
16 个视频
Overfitting demo7分钟
Overfitting for more general multiple regression models3分钟
Balancing fit and magnitude of coefficients7分钟
The resulting ridge objective and its extreme solutions5分钟
How ridge regression balances bias and variance1分钟
Ridge regression demo9分钟
The ridge coefficient path4分钟
Computing the gradient of the ridge objective5分钟
Approach 1: closed-form solution6分钟
Discussing the closed-form solution5分钟
Approach 2: gradient descent9分钟
Selecting tuning parameters via cross validation3分钟
K-fold cross validation5分钟
How to handle the intercept6分钟
A brief recap1分钟
5 个阅读材料
Slides presented in this module10分钟
Download the notebook and follow along10分钟
Download the notebook and follow along10分钟
Observing effects of L2 penalty in polynomial regression10分钟
Implementing ridge regression via gradient descent10分钟
3 个练习
Ridge Regression18分钟
Observing effects of L2 penalty in polynomial regression14分钟
Implementing ridge regression via gradient descent16分钟

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关于 机器学习 专项课程

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
机器学习

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