Chapter 1, Working with Numpy Arrays, explains the basics of numerical computing with NumPy, which is a Python library for working with multi-dimensional arrays and matrices used by scientific computing applications.
Chapter 2, Linear Algebra with Numpy, covers the basics of linear algebra and provides practical NumPy examples.
Chapter 3, Exploratory Data Analysis of Boston Housing Data with NumPy Statistics, explains exploratory data analysis and provides examples using Boston Housing Dataset.
Chapter 4, Predicting Housing Prices Using Linear Regression, covers supervised learning and provides a practical example for predicting housing prices using linear regression.
Chapter 5, Clustering Clients of a Wholesale Distributor Using NumPy, explains unsupervised learning and provides a practical example of a clustering algorithm to model a wholesale distributor sales dataset, which contains information on annual spending in monetary units for diverse product categories.
Chapter 6, NumPy, SciPy, Pandas, and Scikit-Learn, shows the relationship between NumPy and other libraries and provides examples of how they are used together.
Chapter 7, Advanced Numpy, explains the advanced considerations of NumPy library usage.
Chapter 8, Overview of High-Performance Numerical Computing Libraries, introduces several low-level, high-performance numerical computing libraries and their relationship with NumPy.
Chapter 9, Performance Benchmarks, takes a deep dive into the performance of NumPy algorithms depending on the underlying high-performance numerical computing libraries.
