Data Science & Machine Learning Notebooks Collection

A comprehensive collection of Jupyter notebooks covering data science, machine learning, and artificial intelligence topics - from basics to advanced concepts.

📚 Table of Contents

• Data Science & Machine Learning Notebooks Collection
  • 📚 Table of Contents
  • 🐍 Python Fundamentals
  • 📊 Data Analysis & Visualization
  • 🤖 Machine Learning
  • 🧠 Neural Networks & Deep Learning
  • 🕷️ Web Scraping & Data Collection
  • 👁️ Computer Vision
  • 🚀 Special Projects
  • 🛠️ Requirements
  • 📦 Installation & Setup
  • 💻 Usage
  • 🤝 Contributing
  • � Contact

🐍 Python Fundamentals

Essential Python programming concepts and basics:

• basics_of_python_001.ipynb - Introduction to Python
• basics_of_python_002_v1.ipynb - Control Flow
• basics_of_python_004_v3.ipynb - Data Structures
• list_in_python(1).ipynb - Working with Lists

📊 Data Analysis & Visualization

Data manipulation, statistical analysis and visualization:

• analytics_n_visualization_bascis.ipynb - Core concepts
• basics_of_matplotlib.ipynb - Matplotlib fundamentals
• basics_of_analytics_reading_data.ipynb - Data loading
• basics_of_analytics_immigration_data.ipynb - Case study

🤖 Machine Learning

Various machine learning algorithms and applications:

• random_forest_example.ipynb - Random Forest Implementation
• association_rule_learning.ipynb - Association Rules
• xgboost_for_better_ai_models.ipynb - XGBoost
• Support-Vector-Machines.ipynb - SVM Classification

🧠 Neural Networks & Deep Learning

Deep learning models and architectures:

• auto_encoders_market_basket_optimization.ipynb - Autoencoders
• autoencoders_fashion_mnist.ipynb - Fashion MNIST
• autoencoders_cifar10.ipynb - CIFAR10 Classification
• churn_modelling_in_ann_v2.ipynb - Customer Churn Prediction

🕷️ Web Scraping & Data Collection

Web data extraction techniques:

• amazon_web_scraping.ipynb - Amazon Product Scraping
• beautiful_soup_demo.ipynb - BeautifulSoup Basics
• Imdb_web_scraping.ipynb - IMDB Movie Data Extraction

👁️ Computer Vision

Image processing and computer vision applications:

• opencv_basics.ipynb - OpenCV Fundamentals
• satellite_image_processing_v4.ipynb - Satellite Image Analysis

🚀 Special Projects

Unique implementations and case studies:

• music_unmixing_with_musdb.ipynb - Audio Processing
• bar_chart_racing_coving19.ipynb - Animated Visualizations

🛠️ Requirements

pandas>=0.25.0
numpy>=1.16.0
matplotlib>=3.2.2
tensorflow>=2.0.0
scikit-learn>=0.21.3
beautifulsoup4>=4.9.0
opencv-python>=4.2.0

📦 Installation & Setup

Clone the repository:

git clone https://github.com/yourusername/data-science-notebooks.git
cd data-science-notebooks

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

Install dependencies:

pip install -r requirements.txt

💻 Usage

Open notebooks in Jupyter Lab: jupyter lab

• Or use VS Code with Jupyter extension
• Most notebooks are compatible with Google Colab

🤝 Contributing

• Fork the repository
• Create your feature branch
• Commit your changes
• Push to the branch
• Open a Pull Request

📱 Contact

For questions or feedback, please open an issue in the repository.