pouyaexe / python-class-2023

Python and Machine Learning Course

This repository contains the course materials for a Python and Machine Learning course. The course is designed for students who have little to no programming experience and want to learn Python and Machine Learning.

Course Structure

The course is divided into 9 sessions:

1. Introduction to Python
2. Variables, Data Types, and Operators
3. Control Flow and Functions
4. Data Structures in Python
5. Object-Oriented Programming
6. Introduction to Machine Learning
7. Supervised Learning
8. Unsupervised Learning
9. Neural Networks and Deep Learning

Each session covers a specific topic and builds upon the previous sessions. The sessions include lectures, examples, and exercises to help students learn the concepts.

Getting Started

To get started with the course, clone the repository to your local machine:

bashCopy code

git clone https://github.com/your-username/python-machine-learning-course.git

Each session has its own folder containing a README file with instructions on what topics will be covered, what examples will be given, and what exercises students should complete.

The course assumes that you have Python 3 installed on your machine. If you don't have Python 3 installed, you can download it from the official Python website: https://www.python.org/downloads/

Prerequisites

To successfully complete the course, you should have:

• Basic computer skills
• Familiarity with the command line interface
• Basic knowledge of algebra and statistics

Resources

Throughout the course, we will be using the following resources:

• Python 3
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Scikit-Learn
• Keras

Links to the documentation for these resources will be provided in each session's README file.

Acknowledgements

The course materials in this repository were developed by [Your Name] based on personal experience and multiple resources, including online tutorials, books, and other courses.