🧠 Python Libraries for Data Science & Machine Learning

Welcome to my collection of hands-on Jupyter notebooks that cover the most essential Python libraries used in Data Science, Machine Learning, and AI.
This repository is designed to help learners and professionals understand the core functionality, real-world use cases, and implementation of each library through practical examples.

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📚 Libraries Covered

🔹 NumPy

• Numerical computing and array operations
• Mathematical functions, broadcasting, and linear algebra

🔹 pandas

• Data manipulation and cleaning
• DataFrames, Series, and handling missing values

🔹 Matplotlib

• Data visualization using plots, charts, and graphs
• Customization for visual storytelling

🔹 Seaborn

• Statistical data visualization
• Easy and beautiful plotting built on top of Matplotlib

🔹 Scikit-learn

• Machine learning algorithms for classification, regression, and clustering
• Data preprocessing, feature scaling, and model evaluation

🔹 PyTorch

• Deep learning framework for neural networks
• Dynamic computation graphs and GPU acceleration

🔹 TensorFlow

• End-to-end platform for machine learning and deep learning
• Building and training models with Keras API