AI Learning Path (ML_git001) — AI Study Project

“Do your best (FYE).”

A collaborative repository for studying and practicing core Artificial Intelligence topics — from classic Machine Learning (ML) to Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), Reinforcement Learning (RL), and Generative AI — using hands‑on notebooks and a clean Git workflow.

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Table of Contents

• Overview
• Project Goals
• Repository Structure & Roadmap
• Prerequisites
• Quick Start
• Running the Notebooks
• Recommended Git Workflow
• Contributing
• Project Members

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Overview

This repository contains Jupyter notebooks that explore essential AI topics with concise, well‑commented examples. The focus is on learning by doing, with minimal dependencies and clear results.

Why this repo? Small, focused notebooks; minimal setup; reproducible outcomes.

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Project Goals

• Build intuition for common AI/ML workflows (preprocess → train → evaluate → iterate).
• Cover a broad scope: ML (tabular), DL (neural nets), CV, NLP, RL (basics), and Generative AI.
• Encourage good engineering habits (version control, meaningful commits, readable code, lightweight dependencies).

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Repository Structure & Roadmap

Current

• MLproj002/ — Jupyter notebooks and related assets for learning modules (ML focus).
• MLproj003/ — Planned/coming soon.

Planned Modules (to be added progressively)

• ai/ml/ — classic ML (scikit‑learn, pipelines, metrics)
• ai/dl/ — deep learning (PyTorch or TensorFlow/Keras)
• ai/cv/ — computer vision (OpenCV, transforms, CNNs)
• ai/nlp/ — natural language processing (tokenization, embeddings, Transformers)
• ai/rl/ — reinforcement learning (intro, basic algorithms)
• ai/genai/ — generative models (LLMs, diffusion — optional)

(Additional folders like utils/, data/, and docs/ may be added as the project grows.)

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Prerequisites

• Python ≥ 3.9 (3.10+ recommended)
• Git (for version control)
• Jupyter (Notebook or Lab)
• Editor: VS Code or any IDE you like

Common Python Packages

Core (for ML & general work):

• numpy, pandas, scikit-learn, matplotlib, jupyter

Deep Learning (choose a stack):

• PyTorch: torch, torchvision
• TensorFlow/Keras: tensorflow, keras

Computer Vision: opencv-python, scikit-image, Pillow

NLP: nltk, spacy, transformers, datasets

RL (optional): gymnasium, stable-baselines3

GenAI (optional): transformers, diffusers

Note: Install packages as needed depending on the modules you run.

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Quick Start

# 1) Clone the repository (after rename)
git clone https://github.com/Pooria-Lari/ai-learning-path.git
cd ai-learning-path

# If the repository hasn't been renamed yet, use the old URL:
# git clone https://github.com/Pooria-Lari/ML_git001.git
# cd ML_git001

# 2) (Optional but recommended) Create a virtual environment
python -m venv .venv
# Windows (PowerShell): .venv/Scripts/Activate.ps1
# Windows (cmd.exe): .venv/Scripts/activate.bat
# macOS/Linux: source .venv/bin/activate

# 3) Install core packages (adjust as needed)
pip install --upgrade pip
pip install numpy pandas scikit-learn matplotlib jupyter

# 4) (Optional) Install extras by module, e.g. for DL + CV
# PyTorch stack OR TensorFlow/Keras
# pip install torch torchvision
# pip install tensorflow keras
# CV, NLP, RL, GenAI (install as needed)
# pip install opencv-python scikit-image Pillow
# pip install nltk spacy transformers datasets
# pip install gymnasium stable-baselines3
# pip install diffusers

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Running the Notebooks

# From the repository root
jupyter notebook   # or: jupyter lab

Open notebooks inside MLproj002/ (current) or future ai/*/ module folders as they are added. Run cells top‑to‑bottom and read inline comments for guidance.

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Recommended Git Workflow

A simple, clear workflow:

# Check your status
git status

# Stage changes
git add <modified_file_or_folder>

# Commit with a meaningful message
git commit -m "Describe what changed and why"

# Push your work
git push

For larger changes, consider feature branches:

git checkout -b feature/<short-topic>
# ... make changes ...
git push -u origin feature/<short-topic>

Commit message tips:

• Use the imperative mood (e.g., "Add preprocessing step", "Fix accuracy calculation").
• Keep it concise; include why when it helps future readers.

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Contributing

1. Open an issue or discussion for significant changes.
2. Use small, focused pull requests with clear descriptions and before/after notes if relevant.
3. Keep notebooks tidy: remove unnecessary outputs, prefer deterministic seeds when possible.

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Project Members

• Pooria Lari — Member #1
• Reza Ahmadi — Member #2
• Ayda Ahadzadeh — Member #3

Do your best (FYE).

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Happy learning, and keep iterating! 🚀