Python Libraries for AI/ML

🎯 Goal

This repository documents my 4-week journey to master essential Python libraries used in Artificial Intelligence (AI) and Machine Learning (ML) — focusing on NumPy, Pandas, Matplotlib, and Seaborn.

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📚 Libraries Covered

Week	Library	Focus Area	Playlist Link
1️⃣	NumPy	Numerical Computing	NumPy Playlist
2️⃣	Pandas	Data Manipulation	Pandas Playlist
3️⃣	Matplotlib	Data Visualization Basics	Matplotlib Video 1
4️⃣	Seaborn	Advanced Visualization	Seaborn Video 1

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🗓️ Learning Plan (4 Weeks)

Week	Focus	Topics	Deliverables
Week 1	NumPy	Arrays, Indexing, Operations, Broadcasting	Notes, Notebooks, Practice Tasks
Week 2	Pandas	DataFrames, Filtering, GroupBy, Merging	Datasets, Code Examples
Week 3	Matplotlib	Plotting Basics & Advanced Visualizations	Graphs & Charts
Week 4	Seaborn	Advanced Data Visualization	Custom Plots, Style Control

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📂 Repository Structure

python-libraries-for-ai-ml/
│
├── 01_NumPy/
│   ├── Notes/
│   ├── Code/
│   └── README.md
│
├── 02_Pandas/
│   ├── Notes/
│   ├── Code/
│   └── README.md
│
├── 03_Matplotlib/
│   ├── Notes/
│   ├── Code/
│   └── README.md
│
├── 04_Seaborn/
│   ├── Notes/
│   ├── Code/
│   └── README.md
│
└── Summary/
    ├── Cheatsheets/
    ├── Practice_Tasks/
    └── Final_Revision.md

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🧩 Tools & Technologies

• Language: Python 3.10+
• Environment: Jupyter Notebook / VS Code
• Libraries Used:
  numpy, pandas, matplotlib, seaborn
• Dataset Examples: CSV files, sample arrays, and visualization data

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⚙️ Setup Instructions

1. Clone this repository

   git clone https://github.com/yourusername/python-libraries-for-ai-ml.git

2. Install dependencies

   pip install -r requirements.txt

3. Open Jupyter Notebook

   jupyter notebook

4. Explore the notebooks inside each library folder.

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📘 Learning Approach

• 🎥 Watch assigned YouTube playlist/videos daily
• 🧾 Take structured notes in the Notes/ folder
• 💻 Practice using Jupyter notebooks in Code/
• 📊 Apply visualization on real datasets in Datasets/

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🧠 Key Learning Outcomes

By the end of this repository, you’ll be able to:

• Perform numerical computation with NumPy
• Conduct data analysis and cleaning using Pandas
• Build custom visualizations with Matplotlib
• Create statistical and AI-focused plots with Seaborn
• Understand how these libraries connect for AI/ML pipelines

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🧾 Requirements

numpy
pandas
matplotlib
seaborn
jupyter

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🏁 Progress Tracker

Week	Library	Status
Week 1	NumPy	✅ Complete
Week 2	Pandas	✅ Complete
Week 3	Matplotlib	✅ Complete
Week 4	Seaborn	✅ Complete

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👩‍💻 Developed by: Hamna Munir
🚀 Purpose: Building a strong Python foundation for AI/ML
📅 Duration: 4 Weeks