🧠 HelloPython-AI-ML

A growing collection of small, well-documented AI & Machine Learning projects built from the ground up — starting with pure NumPy, expanding into OpenCV, Matplotlib, PyTorch, and Keras.

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🌱 Overview

HelloPython-AI-ML is a learning-oriented repository focused on building and understanding machine learning, deep learning, and computer vision concepts from scratch — not just using prebuilt models.

This repo begins with bare-metal implementations using NumPy for foundational math and logic, then gradually evolves toward using OpenCV, Matplotlib, PyTorch, and Keras for practical AI and ML workflows.

The idea is simple:

• Start small, build deep understanding.
• Evolve your codebase as your skills grow.

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🧩 Roadmap

Stage	Focus	Libraries	Description
Phase 1	Core Math & ML Foundations	numpy, matplotlib	Linear algebra, data manipulation, and simple visualizations.
Phase 2	Image Processing & Computer Vision	opencv-python, numpy	Working with image data, filters, edge detection, etc.
Phase 3	Neural Networks from Scratch	numpy	Implement perceptrons, feed-forward nets, and backprop manually.
Phase 4	Deep Learning Frameworks	pytorch, keras	Move from theory to scalable implementations.
Phase 5	Applied Projects	mixed	Small projects: digit recognition, image classification, clustering, etc.

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📁 Repository Structure

HelloPython-AI-ML/
│
├── README.md               <- This file
├── CONTRIBUTING.md         <- Guidelines for contributors
├── requirements.txt        <- Dependencies list
│
├── numpy_basics/           <- NumPy-only foundational projects
│   ├── linear_algebra.py
│   ├── simple_regression.py
│   └── matrix_operations.py
│
├── opencv_projects/        <- Image and video processing demos
│   ├── edge_detection.py
│   ├── color_space_conversion.py
│   └── object_tracking.py
│
├── matplotlib_visuals/     <- Plotting and data visualization examples
│   ├── loss_curve_demo.py
│   └── scatter_clusters.py
│
├── pytorch_learning/       <- Intro ML/DL projects using PyTorch
│   ├── linear_regression_pytorch.py
│   ├── cnn_mnist.py
│   └── autoencoder_demo.py
│
└── keras_learning/         <- Experiments using TensorFlow/Keras
├── sequential_basics.py
└── image_classifier.py

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🧠 Example: Simple NumPy Project

# numpy_basics/simple_regression.py

import numpy as np
import matplotlib.pyplot as plt

# Generate random linear data
X = np.linspace(0, 10, 100)
y = 3 * X + 4 + np.random.randn(100) * 2

# Linear regression using closed-form solution
X_b = np.c_[np.ones((100, 1)), X]  # Add bias term
theta_best = np.linalg.inv(X_b.T @ X_b) @ X_b.T @ y

# Predictions
y_pred = X_b @ theta_best

plt.scatter(X, y, label="Data")
plt.plot(X, y_pred, color="red", label="Best Fit")
plt.legend()
plt.title("Simple Linear Regression (NumPy)")
plt.show()

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🚀 Getting Started

1️⃣ Clone the repository

git clone https://github.com/gluppler/HelloPython-AI-ML.git
cd HelloPython-AI-ML

2️⃣ Create a virtual environment

python -m venv venv
source venv/bin/activate   # On Windows use: venv\Scripts\activate

3️⃣ Install dependencies

pip install -r requirements.txt

Example requirements.txt:

numpy
matplotlib
opencv-python
torch
torchvision
tensorflow
keras

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🧑‍💻 Contribution

Contributions are welcome! To contribute:

1. Fork the repo
2. Create a new branch (feature/new-idea)
3. Commit your changes
4. Open a Pull Request

Read CONTRIBUTING.md for full guidelines.

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🎯 Goals

• Build intuition for AI/ML concepts through minimal, self-contained examples
• Transition from NumPy-only logic → to PyTorch/Keras abstractions
• Provide a hands-on playground for AI learners, students, and engineers

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📚 Learning References

• NumPy Documentation
• OpenCV Documentation
• Matplotlib Documentation
• PyTorch Tutorials
• Keras Guide

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🧙‍♂️ Author

Gabe Chew (gluppler) Software & Security Engineer | AI/ML Enthusiast | Creator of Cult of the LOLCOW & Behelit Systems

"Understanding comes from building — not importing."

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🏷️ License

This project is licensed under the GNU General Public License – see the LICENSE file for details.