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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

  1. 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.