This project demonstrates fundamental matrix operations using the Python NumPy library. It includes key operations such as matrix addition, subtraction, scalar multiplication, matrix multiplication, matrix inversion, and solving linear equations using matrix division (via inverse). These concepts are essential in fields like linear algebra, data science, physics, and machine learning.
The code provides a beginner-friendly implementation of each operation using clearly defined examples. It helps users understand how numerical data can be manipulated and transformed using linear algebra techniques in Python. The use of NumPy allows for efficient and clean matrix handling, which is particularly useful for handling large datasets and performing complex computations.
- Matrix addition and subtraction
- Scalar multiplication of a matrix
- Matrix-matrix multiplication (dot product)
- Matrix inversion using
np.linalg.inv() - Solving linear systems using matrix division (
A⁻¹B) - Clean print statements for step-by-step results
This project serves as a practical introduction to linear algebra using Python. By running the script, users will gain hands-on experience with key matrix operations and develop a better understanding of how these concepts apply to real-world problems in data analysis, engineering, and beyond. The code can be expanded further to include more advanced topics like eigenvalues, eigenvectors, and Singular Value Decomposition (SVD), making it a solid foundation for deeper learning.