Learn AI and ML with Python

This project helps you learn basic implementation of machine learning data mining algorithms

Pre-requists

In order to have clear understanding of this code you must know following things.

1. Pyhton
2. How to Run Jupyter Notebook

Tools

In order to running the following code you must have following things installed on your computer.

1. Python
2. Anaconda

How to run the codes

1. Clone this repository to your local machine by either clicking on clone button or you can do it form git bash or linux terminal using following command.

git clone https://github.com/owais4321/Learn-Machine-Learning

2. Once you have codes on your local machine now run jupyter on your machine then upload the code and respective dataset to jupyter home.
3. Now you have codes in your jupyter repository or folder now you can see your project on home in jupyter now click on and a new windows with browser will be opened up now click run button and you will see the results.

How to contribute

1. Fork this repository
2. clone that repository

git clone link_of_that_repository

3. Make changes that you want in local repository
4. Add those changes

git add .

5. commmit those changes

git commit -m 'name of commit'

6. push changes to remote repository
7. create pull request

What to contribute

Following contribution can done in this project.

1.Update readme file for association rules how they work and what association rules have been used and thier overview.

2.Update readme file for classification how it works and what classification algorithm have been used and thier overview.

3.Update readme file for clustering how it works and what clustering algorithm have been used and thier overview.

4.Update readme file for text classification how it works and what classification algorithm have been used and thier overview.

5.Update readme file for sentiment analysis how it works and what classification algorithm have been used and thier overview.

6.Translate Readme into multiple languages.

7. Translate intstructions in jupyter notebook and comments into multiple languages.

8. Add New Algorithm into classification.

9. Add new Algorithm into clustering.

10. Add new Algorithm for text classification.

11. Add new Algorithm for sentiment analysis.

12. Update readme for dataset and give description of datasets.