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Day 14 – NumPy Matrix Operations

Matrix operations are a core part of numerical computing and data science. In NumPy, matrices are represented as multidimensional arrays, and various mathematical operations can be performed easily and efficiently.

In this lesson, we’ll learn how to perform basic and advanced matrix operations using NumPy. These include:

Addition & Subtraction – Performing element-wise operations using + and -.

Matrix Multiplication – Using np.dot() or the @ operator to multiply matrices.

Transpose – Using .T or np.transpose() to flip rows and columns.

Inverse – Using np.linalg.inv() to find the inverse of a square matrix.

Determinant – Using np.linalg.det() to calculate the determinant of a matrix.

Matrix operations are the backbone of machine learning, linear algebra, and scientific simulations. Understanding these functions will help you handle complex mathematical computations efficiently using NumPy.