NumPy

Scientific Computing

Fundamental package for scientific computing with Python. Provides support for arrays, matrices, and mathematical functions.

Installation
pip install numpy
Documentation
Official Docs

Chapters Using NumPy

Chapter 1: Linear Algebra for ML (Mathematical Foundations for Machine Learning)
Primary
Chapter 2: Calculus and Optimization (Mathematical Foundations for Machine Learning)
Primary
Chapter 3: Probability and Statistics (Mathematical Foundations for Machine Learning)
Primary
Chapter 4: Python Data Science Stack (Python Ecosystem and Classical Machine Learning)
Primary
Chapter 9: Neural Network Foundations (Deep Learning Fundamentals)
Chapter 29: Data Acquisition & Wrangling (Data Science: Process & Practice)
Chapter 30: Exploratory Data Analysis (Data Science: Process & Practice)
Chapter 31: Feature Engineering & Selection (Data Science: Process & Practice)
Chapter 32: Data Visualization & Storytelling (Data Science: Process & Practice)
Chapter 33: Descriptive Statistics & Distributions (Statistics & Statistical Inference)
Chapter 34: Probability Distributions in Depth (Statistics & Statistical Inference)
Chapter 35: Sampling & Estimation (Statistics & Statistical Inference)
Chapter 36: Hypothesis Testing (Statistics & Statistical Inference)
Chapter 37: Resampling & the Bootstrap (Statistics & Statistical Inference)
Chapter 38: Bayesian Inference (Statistics & Statistical Inference)
Chapter 39: Regression & Generalized Linear Models (Statistics & Statistical Inference)
Chapter 40: Experimental Design & A/B Testing (Statistics & Statistical Inference)
Chapter 45: NoSQL & Modern Data Stores (Databases & SQL for Data Science)
Chapter 46: Time Series Fundamentals (Time Series Analysis & Forecasting)
Chapter 47: Classical Forecasting: ARIMA & ETS (Time Series Analysis & Forecasting)
Chapter 48: Machine Learning for Time Series (Time Series Analysis & Forecasting)
Chapter 49: Python for Data Science in Depth (Programming for Data Science)
Chapter 51: Reproducible Research & Notebooks (Programming for Data Science)