▶Book Description
NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts.
Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system.
By the end of this book, you will have become an expert in handling and performing complex data manipulations.
▶What You Will Learn
⦁ Perform vector and matrix operations using NumPy
⦁ Perform exploratory data analysis (EDA) on US housing data
⦁ Develop a predictive model using simple and multiple linear regression
⦁ Understand unsupervised learning and clustering algorithms with practical use cases
⦁ Write better NumPy code and implement the algorithms from scratch
⦁ Perform benchmark tests to choose the best configuration for your system
▶Key Features
⦁ Grasp all aspects of numerical computing and understand NumPy
⦁ Explore examples to learn exploratory data analysis (EDA), regression, and clustering
⦁ Access NumPy libraries and use performance benchmarking to select the right tool
▶Who This Book Is For
Mastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book.
▶What this book covers
⦁ 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.
