# Stanford机器学习笔记-1.线性回归

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Content：

1. Linear Regression

1.1 Linear Regression with one variable

1.2 Linear Regression with multiple variable

1.2.1 Feature Scaling

1.2.2 Features and polynomial regression

1.2.3 Normal equation

1.2.4 Probalilistic interpretation for cost function

key words:

Linear Regression, Gradient Descent, Learning Rate, Feature Scaling, Normal Equation

1. Linear Regression

1.1 Linear Regression with one variable

learning rate:

1.2 Linear Regression with multiple variables

1.2.1 Feature Scaling（数据规范化）

1.2.2 Features and polynomial regression

1.2.3 Normal equation（正则方程）

Normal equation: Method to solve for analytically.

m = 20000, n = 10000，优先考虑Gradient Descent

m = 20000, n = 10, 优先考虑Normal Equation

1.2.4 Probalilistic interpretation for cost function

• 发表于:
• 原文链接http://kuaibao.qq.com/s/20180206A0HWMZ00?refer=cp_1026
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