# Coursera-Stanford-ML-Python

**Repository Path**: cui_xinhong/Coursera-Stanford-ML-Python

## Basic Information

- **Project Name**: Coursera-Stanford-ML-Python
- **Description**: Coursera/Stanford Machine Learning course assignments in python
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-07-02
- **Last Updated**: 2021-07-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Coursera-Stanford-ML-Python

Coursera/Stanford Machine Learning course assignments in Python

[![Join the chat at https://gitter.im/sgang007/Coursera-Stanford-ML-Python](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/sgang007/Coursera-Stanford-ML-Python?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

Assignments for Andrew Ng's Machine Learning course implemented in Python without solutions in line with the [Coursera Code of Honor](https://www.coursera.org/about/terms/honorcode "Coursera Honor Code"). The code is structurally equivalent to the Matlab implementation from Coursera and the results are numerically equivalent with the correct Python implementation of the incomplete scripts. After completing each assignment, students can submit for grading to the Coursera website by executing the submit.py script. e.g below: (OSX or Linux) (On Windows change "export PYTHONPATH=../" to "set PYTHONPATH=..\")

```
cd Coursera-Stanford-ML-Python/ex1
export PYTHONPATH=../
python submit.py

login (Email address): 
token: 
==
==                                   Part Name |     Score | Feedback
==                                   --------- |     ----- | --------
==                            Warm up exercise |  10 /  10 | Nice work!
==           Computing Cost (for one variable) |   0 /  40 | 
==         Gradient Descent (for one variable) |   0 /  50 | 
==                       Feature Normalization |   0 /   0 | 
==     Computing Cost (for multiple variables) |   0 /   0 | 
==   Gradient Descent (for multiple variables) |   0 /   0 | 
==                            Normal Equations |   0 /   0 | 
==                                   --------------------------------
==                                             |  10 / 100 |
```

The login credentials will be saved to a file for subsequent submissions. Please see the [wiki](https://github.com/mstampfer/Coursera-Stanford-ML-Python/blob/master/Coursera%20Stanford%20ML%20Python%20wiki.ipynb) for a short tutorial on using Python.