🔢 NumPy for Scientific Computing

This repository contains my practice files and exercises for mastering NumPy, the fundamental package for scientific computing in Python. A deep understanding of NumPy is essential for any serious work in data science, machine learning, and AI, as it provides the high-performance, multi-dimensional array object that underpins almost the entire ecosystem.

The scripts and notebooks here are my hands-on work in learning to perform efficient numerical operations on large datasets.

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🎯 Core NumPy Concepts Covered

This collection demonstrates my practical experience with the core features of the NumPy library, including:

• The ndarray Object:
  • Creating N-dimensional arrays from scratch (np.array, np.zeros, np.ones, np.arange).
  • Understanding array attributes (.shape, .dtype, .ndim).
• Indexing & Slicing:
  • Advanced indexing techniques for accessing and modifying array subsections.
  • Boolean array indexing for filtering data.
• Array Manipulation & Operations:
  • Reshaping, stacking, and splitting arrays.
  • Performing fast, element-wise arithmetic operations.
• Broadcasting:
  • Understanding and applying the rules of broadcasting to perform operations on arrays of different shapes.
• Universal Functions (ufuncs):
  • Applying mathematical functions (np.sqrt, np.exp, np.sin) across entire arrays without explicit loops.
• Basic Linear Algebra & Statistics:
  • Matrix multiplication (@ operator).
  • Calculating aggregates like mean, median, sum, and standard deviation.

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📚 My Learning Pathway

This repository is a core component of my self-directed curriculum.

• Step 1: Python Foundations
• ➡️ Step 2: Data Analysis with Pandas & NumPy (You are here)
• Step 3: Data Visualization with Matplotlib
• Step 4: Computer Vision with OpenCV

Feel free to explore the files to see my implementation of these fundamental scientific computing concepts.