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Samra Fakhar

Linear algebra comes into play in the data science and machine learning domain a lot. **NumPy** is the scientific computing library for Python, which provides several linear algebra functionalities. NumPy also provides the module required for linear algebra in the form of `linalg`

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Below, are some common linear algebra operations that use NumPy.

import numpy as np from numpy import linalg A = np.array([[1, 2, 1], [4, 9, 5], [4, 8, 11]]) print("Rank of matrix A:", linalg.matrix_rank(A)) print("Determinant of matrix A:", linalg.det(A)) print("Inverse of A:", linalg.inv(A))

import numpy as np from numpy import linalg arr = np.array([[3, -4j], [5j, 6]]) print("Given Array:",arr) e1, e2 = linalg.eigh(arr) print("Eigen value is :", e1) print("Eigen value is :", e2)

import numpy as np # x + y = 6 # −3x + y = 2 arr1 = np.array([[1, 1], [-3, 1]]) arr2 = np.array([6, 2]) arr = np.linalg.solve(arr1, arr2) print ('x =', arr[0]) print ('y =', arr[1])

import numpy as np # dot product of two vectors a = np.array([1+2j,3+4j]) b = np.array([5+6j,7+8j]) product = np.vdot(a, b) print("Dot Product : ", product) # inner product of arrays a = np.array([1,2,3]) b = np.array([0,1,0]) product = np.inner(a, b) print("Inner Product : ", product) # matrix multiplication a = np.array([[1, 0], [0, 1]]) b = np.array([[4, 1], [2, 2]]) product = np.matmul(a, b) print("Product of Matrices : ", product)

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