課程信息
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第 2 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

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完成時間大約為36 小時

建議:6 weeks of study, 5-8 hours/week...

英語(English)

字幕:英語(English), 韓語, 阿拉伯語(Arabic)
User
學習Course的學生是
  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • Biostatisticians
  • Risk Managers

您將獲得的技能

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis
User
學習Course的學生是
  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • Biostatisticians
  • Risk Managers

第 2 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

完成時間大約為36 小時

建議:6 weeks of study, 5-8 hours/week...

英語(English)

字幕:英語(English), 韓語, 阿拉伯語(Arabic)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 1 小時

Welcome

5 個視頻 (總計 20 分鐘), 3 個閱讀材料
5 個視頻
Welcome!1分鐘
What is the course about?3分鐘
Outlining the first half of the course5分鐘
Outlining the second half of the course5分鐘
Assumed background4分鐘
3 個閱讀材料
Important Update regarding the Machine Learning Specialization10分鐘
Slides presented in this module10分鐘
Reading: Software tools you'll need10分鐘
完成時間為 3 小時

Simple Linear Regression

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

Multiple Regression

19 個視頻 (總計 87 分鐘), 5 個閱讀材料, 3 個測驗
19 個視頻
Polynomial regression3分鐘
Modeling seasonality8分鐘
Where we see seasonality3分鐘
Regression with general features of 1 input2分鐘
Motivating the use of multiple inputs4分鐘
Defining notation3分鐘
Regression with features of multiple inputs3分鐘
Interpreting the multiple regression fit7分鐘
Rewriting the single observation model in vector notation6分鐘
Rewriting the model for all observations in matrix notation4分鐘
Computing the cost of a D-dimensional curve9分鐘
Computing the gradient of RSS3分鐘
Approach 1: closed-form solution3分鐘
Discussing the closed-form solution4分鐘
Approach 2: gradient descent2分鐘
Feature-by-feature update9分鐘
Algorithmic summary of gradient descent approach4分鐘
A brief recap1分鐘
5 個閱讀材料
Slides presented in this module10分鐘
Optional reading: review of matrix algebra10分鐘
Exploring different multiple regression models for house price prediction10分鐘
Numpy tutorial10分鐘
Implementing gradient descent for multiple regression10分鐘
3 個練習
Multiple Regression18分鐘
Exploring different multiple regression models for house price prediction16分鐘
Implementing gradient descent for multiple regression10分鐘
3
完成時間為 2 小時

Assessing Performance

14 個視頻 (總計 93 分鐘), 2 個閱讀材料, 2 個測驗
14 個視頻
What do we mean by "loss"?4分鐘
Training error: assessing loss on the training set7分鐘
Generalization error: what we really want8分鐘
Test error: what we can actually compute4分鐘
Defining overfitting2分鐘
Training/test split1分鐘
Irreducible error and bias6分鐘
Variance and the bias-variance tradeoff6分鐘
Error vs. amount of data6分鐘
Formally defining the 3 sources of error14分鐘
Formally deriving why 3 sources of error20分鐘
Training/validation/test split for model selection, fitting, and assessment7分鐘
A brief recap1分鐘
2 個閱讀材料
Slides presented in this module10分鐘
Polynomial Regression10分鐘
2 個練習
Assessing Performance26分鐘
Exploring the bias-variance tradeoff8分鐘
4
完成時間為 3 小時

Ridge Regression

16 個視頻 (總計 85 分鐘), 5 個閱讀材料, 3 個測驗
16 個視頻
Overfitting demo7分鐘
Overfitting for more general multiple regression models3分鐘
Balancing fit and magnitude of coefficients7分鐘
The resulting ridge objective and its extreme solutions5分鐘
How ridge regression balances bias and variance1分鐘
Ridge regression demo9分鐘
The ridge coefficient path4分鐘
Computing the gradient of the ridge objective5分鐘
Approach 1: closed-form solution6分鐘
Discussing the closed-form solution5分鐘
Approach 2: gradient descent9分鐘
Selecting tuning parameters via cross validation3分鐘
K-fold cross validation5分鐘
How to handle the intercept6分鐘
A brief recap1分鐘
5 個閱讀材料
Slides presented in this module10分鐘
Download the notebook and follow along10分鐘
Download the notebook and follow along10分鐘
Observing effects of L2 penalty in polynomial regression10分鐘
Implementing ridge regression via gradient descent10分鐘
3 個練習
Ridge Regression18分鐘
Observing effects of L2 penalty in polynomial regression14分鐘
Implementing ridge regression via gradient descent16分鐘
4.8
832 個審閱Chevron Right

45%

完成這些課程後已開始新的職業生涯

43%

通過此課程獲得實實在在的工作福利

18%

加薪或升職

來自Machine Learning: Regression的熱門評論

創建者 PDMar 17th 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!

創建者 CMJan 27th 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!

講師

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Emily Fox

Amazon Professor of Machine Learning
Statistics
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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

關於 华盛顿大学

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world....

關於 机器学习 專項課程

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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