NumPy Basics for Machine Learning

NumPy is a Python library for working with arrays and performing fast numerical operations, making it a core machine learning tool.

Creating Arrays

NumPy arrays are similar to lists, but much faster and more efficient for numerical work.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

print(arr)

Array Operations

You can perform mathematical operations on entire arrays at once, without writing loops.

arr = np.array([1, 2, 3, 4])

print(arr + 10)

print(arr * 2)

Creating Special Arrays

NumPy provides quick ways to create arrays filled with zeros, ones, or a range of numbers.

zeros = np.zeros(5)

ones = np.ones(3)

range_arr = np.arange(1, 10, 2)

Reshaping Arrays

Reshaping lets you change the structure of data, which is often needed before feeding it into a machine learning model.

arr = np.array([1, 2, 3, 4, 5, 6])

reshaped = arr.reshape(2, 3)

print(reshaped)

Basic Statistics with NumPy

NumPy makes it easy to calculate common statistics used throughout machine learning.

data = np.array([10, 20, 30, 40, 50])


print(np.mean(data))

print(np.median(data))

print(np.std(data))

NumPy is the backbone of numerical computing in Python, and libraries like Pandas and Scikit-learn are built directly on top of it.