## 在线资料
中文资料：https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes
在线文章：http://www.ai-start.com/ml2014/html/week1.html#header-n257


## Linear Regression Model

**mind**

1. what, fitting a straight line to your data 
2. how, build a linear regression model from this dataset 
    1. use a dataset on house sizes and prices
    2. plot a graph where the horizontal axis is the size of the house in square feet, and the vertical axis is price of a house 
    3. plot the data points
    4. fit a straight line to the data 

3. estimate the price(how, predict the price of a house based on the size of the house)
    1. input the size of the house 
    2. output price


**key word**


1. first model
2. That just means fitting a straight line(直线) to your data.
3. let's start with a problem that you can address using linear regression.
4. Say you want to predict the price of a house based on the size of the house.
5. We're going to use a dataset on house sizes and prices from Portland
6. Here we have a graph  where the horizontal axis is the size of the house in square feet, and the vertical axis is the price of a house in thousands of dollars.
7. Let's go ahead and plot the data points for various houses in the dataset.
8. Supervised learning model
9. Data has "right answers"
10. Regression model 
11. Predicts numbers 
12. Regression problem 



### Part One



### Part Two

key word
best-fit line

the linear function

why we are choosing a  linear function

linear regression

linear regression with one variable = univariate linear rgression

construct a cost function

is the most universal and important ideas in machine learning and is  used in both linear regression and in training many of the most advanced AI models in the world

从示例分析流程


![image.png](https://bobloveanita.com/static/img/b743c517815ff1b60688ba1084963e9b.image.webp)

 抽象出公式
 
![image.png](https://bobloveanita.com/static/img/7611fbc2602e613349bdf2482ba52a5f.image.webp)

### Cost Function

**mind**
1. what
2. why need
3. how to formula

**key process**

**key word **
1. parameters、coefficients、weights
2. hellp you build intuition on how w and b the parameters determine  f 
3. prediction，predictive value
4. slop of the line
5. line intersects
6. superscript ？ 上标
7. y hat i
8. close to the true targe
9. Now the question is how  do you find values for w and b so that the predict y hat i is close to the ture target  y^i for many or maybe all training examples x^i y^i 
10. how to measure how well a line fits the training  data
11. to do that , we're going to construct a cost function  
12. The cost function takes the prediction y hat and compares it to the target y by taking y hat minus(减)  y.
13. This different called  thd error .
14. let's computes the square 平方 of this error.
15. we're going to want to  compute this term 项 for different  training examples i in the training set .
16. we want to measure the error across the entire 全部 training set.
17. m is the number of training set examples 
18. notice that if we have more training examples m is larger and your   cost function will calculate a bigger number
19. To build a cost function that doesn't automatically get bigger  as the training set size gets larger by convention,we will compute average squared error  instead of the total squared error  and we do that by dividing by m like this.
20. By convention the cost function that machine learning people use  actually divides by 2 times m. 
21. The extra division by 2 is just meant to make some of oue later calculations look neater, but the cost function  still works whether you include this division by 2 or not.


![image.png](https://bobloveanita.com/static/img/40ba3558c5c1f1facf8e50900e2b06eb.image.webp)
![image.png](https://bobloveanita.com/static/img/a5cf5ed415223054c0fcea113228c7e8.image.webp)


![image.png](https://bobloveanita.com/static/img/fa0ed88b89f2f5efa6d0a168019c5af3.image.webp)

### Cost Function inition
**key process**
1. define goal
    1. model: you want to fit a straight line to the training data, so you have this model , fw 
    2. parameters : the model's parameters are w, and b,
        1. depending on the values chosen for these parameters
        2. ,you get different straight lines like this. 
        3. you want to find values for w, and b, so that the straight line fits the training data well
    3. cost function 
        1. To measure how well a choice of w,and b fits the training data ,you have cost functiuon J.
        2. What does cost function J dose is, it measure different between the model's predictions and the actual true values for y.
        3. wht you see later, is that linear regression would try to find values for w, and b , then make a J of w be as small as possible.
     4. goal: in math, we want to minimize J
         1.  
2. simplified
    1. in order  for us to better visualize the cost function J.
    2. getting rid of the  parameter  b
    3. It just goes away from the equation,
    4. so f is now just w times x
    5. the goal become J of w 

3. f and j are related
    1. f function , w is constant value , f is only a function of x.
    2. j  is only a function of w.
    3. first pick value 
        1. the plots of three points representing，1,1 2,2 3,3
        2. pick a value for w, Say w is 1. 似乎和上一步顺序颠倒，应该先有w，然后在选择f的点位
        3. compute f and j of three point, cost function J is equal to 0 .
    4. let's look at how F and J change for differrent values of w.
    5. second pick value 
        1. w is equal to 0.5
        2. comput to  y hat  of three point
        3. comput to  J，we work out the math
        4. The cost J is about 0.58
        5. plot J graph

    6. pick too much values
        1. comput to  J，we work out the math
        2. plot J graph
     7. how to choose w ? 
         1. Well, as you can imagine, choosing a value of w that casues J of w to be as small as possible seems like good bet. 
     8. J is cost function that measures hot big the squared errors are, so choosing w that minimizes these squared errors, makes them as small as possible, will give us a good model.


**key word**
1. how the cost function can be used to  find the best parameters for your model
2. you want to fit a straight line to the training data 
3. you have , model , parameters, cost function 
4. goal 

**coursera ppt**


![image.png](https://bobloveanita.com/static/img/2a55a2779a61a24143bb34846b30d25c.image.webp)


![image.png](https://bobloveanita.com/static/img/31c1f6a9a52a7479d628a2af46b5ef5c.image.webp)

![image.png](https://bobloveanita.com/static/img/23d0f0b3cf6b230722c868cdfd6eff6e.image.webp)

吴恩达-ML学习

> 需求分析，本质信息传递，并对信息解读的过程
> 
> 客户将需求信息传递给售前，售前对信息做分析处理。

从两个层面介绍
1. 方法论
2. 常见的问题
    1. 问题点
    2. 解决方案
    3. 示例

可以从两个方面介绍需求分析
1. 场景
2. 项目

## 需求信息的失真与噪声
需求传递通常的流程
1. 客户方，存在一个业务问题
2. 客户方的A员工发现了这个问题
3. A员工将信息传递给信息部门或IT部门
4. 信息部门或IT部门 与 服务商（乙方）反馈沟通

可能存在的情况
1. 信息经过多人传递，最后传递到服务商的信息存在失真
2. 传递过程中，某个人将自己的主观判断或决策也添加到需求信息中

售前需求分析

数字技术赋能业务的五个阶段



![image.png](https://bobloveanita.com/static/img/5e813bea6eb206c056bb52175ac6cd33.image.webp)

## 每个阶段的意义与价值
提高生产效率，提供决策依据，促进柔性生产
待完善：书籍 决胜B端，以下内容

ToB数字化的意义与价值

> **记录可写的主题**



## 待写作主题
1. 我是如何摆脱完美主义
2. 首次参加线下读书会，我收获了什么
3. 读书感受，又开启了我的第二段疯狂输入阶段，有什么改变，集中的方面、读书的技巧

写作-待写主题

> **拥有你自己的原则**



## 生活原则

### 用5步流程实现你的人生愿望

1. 有明确的目的
2. 找到阻碍你实现这些目标的问题，并且不容忍问题
3. 准确诊断问题，找到问题的根源
4. 规划可以解决问题的方案
5. 做一切必要的事来践行这些方案，实现成果。


![image.png](https://bobloveanita.com/static/img/01468b0744c8bf2cc619425125bc6243.image.webp)


### 做到头脑极度开放
1. 人生你的两大障碍
2. 奉行头脑极度开放
3. 领会并感激：深思熟虑的意见分歧
4. 和可信的、愿意表达分歧的人一起审视你的观点
5. 识别你应当注意的头脑封闭和头脑开放的不同迹象
6. 理解你如何做到头脑极度开放


### 学习如何有效决策

1. 要认识到：影响好决策的最大威胁是有害的情绪；决策是一个步流程（先了解后决定）
2. 综合分析眼前的形势
3. 综合分析变化中的形势
4. 高效地综合考虑各个层次
5. 综合分析现实、理解如何行动的最好工具是逻辑、理性和常识
6. 根据预期价值计算做决策
7. 比较更多信息带来的价值和不做决定造成的成本，决定优先顺序
8. 简化
9. 使用原则
10. 对你的决策进行可信度加权
11. 把你的原则转换成算法，让计算机和你一起决策