Intro to Machine Learning (ML), Matrices & NumPy

Installation: conda install numpy

import numpy as np

my_matrix = [[1,2,3],[4,5,6],[7,8,9]]

np.array(my_list) OR np.array(my_matrix)

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np.arange(0,10) // Creating an array from 0 to 9

np.arange(0,11,2) / with step size of 2

Generate Zeros and Ones: np.zeros(3) // 1 D

np.ones((3,3)) // 2D

Return evenly line spaced: np.linspace(0,10,3) // last number represents the number / how many

Create identity matrix: np.eye(4) // 2D square matrix; diagonal is 0 the rest is 0

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Create a random matrix: np.random.rand(5,5) // create an array uniformly distributed.

Create a standard normal distribution: np.random.randn(5,5) // from standard / normal distribution

Create a random integer from low to high: np.random.randint(1,100,10) / random integer from low to

high number.

np.random.rantint(5,100) // create a random value from 5 to 100

arr = np.arange(25)

ranarr = np.random.mandint(0,50,10)

arr.reshape() // reshape value

arr.shape // call the shape of data

ranarr.min()

ranarr.max()

ranarr.arnmax()

ranarr.argmin()

arr.dtype

Vector - 1D array

Matrices - 2D array