This is a practicing coding for Andrew Ng's course of Machine Learning, which will be submitted to the Coursera.
这是我对于吴恩达的机器学习课程的学习中所做的练习代码，将会被提交给Coursera进行评分。
Learning this course using the "Andrew Ng's Machine Learning" published by "Netease Cloud Classroom"
课程学习使用“网易云课堂”中的《吴恩达机器学习》视频系列。
Submitting the answer from the Coursera's system, since I attened to the same online class starts from 26 Feb., 2019.
提交作业使用Coursera的作业系统，我参加了2019年2月26日(?)开始的Coursera课程。
1.Exercise-1 has been accpeted by the system on 23 March 2015, since I have been enrolled into this class (marked 100/100). For the time gap is too long, I would check this code later.
因为以前曾参加过此课程，练习-1已在2015年3月23日被成功提交，分数为100/100。时间间隔太长，我将会后续补上此代码。
2.This is a record for my own coding process, and I would like you to download, explore or play with these codes。 But I am STRONGLY NOT RECOMMEND you to submit this code to the Coursera directly, because it is a VIOLATION to the HONOR CODE.
此项目记录了我自己的代码编写过程，我十分欢迎您下载、研究、探索这份作业的代码。但是我强烈不推荐您将这份代码毫无改动的当作您自己的作业上传至Coursera，因为这样已经违反了诚信协议。
machine-learning-ex1
Submitted on 23 March 2015 at 3:59 PM
提交时间 23 March 2015 在 3:59 PM
Score(成绩) 100%
| Part 部分 |
Name 名称 |
Socre 分数 |
|---|---|---|
| 1 | Warm up exercise | 10 / 10 |
| 2 | Compute cost for one variable | 40 / 40 |
| 3 | Gradient descent for one variable | 50 / 50 |
| 4 | Feature normalization | 0 / 0 |
| 5 | Compute cost for multiple variables | 0 / 0 |
| 6 | Gradient descent for multiple variables | 0 / 0 |
| 7 | Normal equations | 0 / 0 |