Python Libraries Guide

This repository contains my learning notes, explanations, and Jupyter notebooks for important Python libraries used in Artificial Intelligence, Data Science, and Machine Learning.

My goal is to create a clear, beginner-friendly reference that helps me (and others) understand these libraries from basics to practical usage.

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Libraries Covered

🔹 NumPy

Fundamentals of numerical computing, array manipulation, mathematical operations, statistics, and random functions.

🔹 Pandas

DataFrames, data cleaning, analysis, grouping, merging, filtering, and EDA.

🔹 Matplotlib

Basic and advanced visualizations, line plots, bar charts, scatter plots, customizations, and styling.

🔹 Seaborn

High-level statistical visualizations including pairplots, heatmaps, distributions, and categorical plots.

More libraries will be added as I learn them.

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📁 Repository Structure

Python-Libraries-Guide/
    ├── NumPy/
    │      ├── README.md
    │      └── NumPy_Basics.ipynb
    ├── Pandas/
    │      ├── README.md
    │      └── Pandas_Basics.ipynb
    ├── Matplotlib/
    │      ├── README.md
    │      └── Matplotlib_Basics.ipynb
    └── Seaborn/
           ├── README.md
           └── Seaborn_Basics.ipynb

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Each folder includes:

• A notebook with explanations and examples
• A small README for that specific library

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Purpose of This Repository

• To document my learning journey in AI & DS
• To build a strong foundation in Python libraries
• To help others who are learning these libraries
• To maintain clean and reusable notes
• To create a beginner-friendly reference

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Tools Used

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn

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Future Additions

• Scikit-Learn
• Plotly
• Statistics Notes
• Machine Learning Basics
• Exploratory Data Analysis Projects

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Feel free to explore and learn along with me! 😊