teaching_ml README.md

teaching_ml

Educational materials and code examples for learning Machine Learning (ML), designed for beginners and intermediate learners to grasp core ML concepts through hands-on practice.

Table of Contents

• Getting Started

  • Clone the Repository

  • Set Up the Conda Environment

• Repository Structure

• Dependencies

• Usage

• Contributing

• License

Getting Started

Clone the Repository

Clone this repository to your local machine using the following command:

git clone https://github.com/BariumOxide13716/teaching_ml.git cd teaching_ml/main

Set Up the Conda Environment

Use a Conda environment to manage dependencies (numpy, tensorflow, matplotlib). Follow these steps:

1. Create a new Conda environment (name: teaching_ml_env; Python 3.9 recommended for TensorFlow compatibility): conda create -n teaching_ml_env python=3.9 -y

2. Activate the environment:

• Windows: conda activate teaching_ml_env

• macOS/Linux: source activate teaching_ml_env

3. Install required packages: # Install individually conda install numpy matplotlib -y conda install tensorflow -y # Use conda install tensorflow-gpu -y for GPU support (check CUDA/cuDNN first)

Or install all at once

conda install -n teaching_ml_env numpy tensorflow matplotlib -y

Repository Structure

The main directory contains:

• Jupyter Notebooks with step-by-step ML tutorials (e.g., linear regression, neural networks, classification)

• Python scripts for standalone ML experiments

• Datasets (if applicable) used in examples

• Supplementary materials (slides, notes) to reinforce ML concepts

Dependencies

• Python 3.8+ (tested with Python 3.9)

• NumPy: For numerical computations

• TensorFlow: For building and training ML models

• Matplotlib: For data and model result visualization

Verify installations with:

import numpy import tensorflow import matplotlib print("NumPy version:", numpy.version) print("TensorFlow version:", tensorflow.version) print("Matplotlib version:", matplotlib.version)

Usage

1. After setting up the environment, launch Jupyter Notebook (for notebook tutorials): jupyter notebook

2. Navigate to the .ipynb files and run the cells to follow the tutorials.

3. For standalone Python scripts, run: python <script_name>.py

Contributing

1. Fork the repository

2. Create a new branch: git checkout -b feature/your-feature

3. Commit your changes: git commit -m 'Add new tutorial on [topic]'

4. Push to the branch: git push origin feature/your-feature

5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.